application of image enhancement and ......1.1 biometrics and palm prints 1 1.2 need for palm print...
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APPLICATION OF IMAGE ENHANCEMENT AND
SEGMENTATION TECHNIQUES ON VEIN PATTERN FOR
BETTER IDENTIFICATION
Thesis submitted in
Partial Fulfillment for the award of
Degree of Doctor of Philosophy
in Computer Science & Engineering
By
S.SHARAVANAN
FACULTY OF ENGINEERING AND TECHNOLOGY
VINAYAKA MISSIONS UNIVERSITY
(VINAYAKA MISSIONS RESEARCH FOUNDATION DEEMED UNIVERSITY)
SALEM, TAMILNADU, INDIA
JANUARY 2016
VINAYAKA MISSIONS UNIVERSITY
SALEM
DECLARATION
I, S. Sharavanan, declare that the thesis entitled
APPLICATION OF IMAGE ENHANCEMENT AND
SEGMENTATION TECHNIQUES ON VEIN PATTERN FOR
BETTER IDENTIFICATION submitted by me for the Degree of Doctor
of Philosophy is the record of work carried out by me during the period
from 2009 to 2016 under the guidance of Dr. A. Nagappan, and has not
formed the basisfor the award of any degree, diploma, associate-ship,
fellowship, or other titles in this University or any other University or
Institution of higher learning.
Place: Salem
Date: Signature of the Candidate
VINAYAKA MISSIONS UNIVERSITY
SALEM
CERTIFICATE BY THE GUIDE
I, Dr. A. Nagappan, certify that the thesis entitled
APPLICATION OF IMAGE ENHANCEMENT AND
SEGMENTATION TECHNIQUES ON VEIN PATTERN FOR
BETTER IDENTIFICATION submitted for the Degree of Doctor of
Philosophy by Mr.S.Sharavanan, is the record of research work carried out
by him during the period from 2009 to 2016 under my guidance and
supervision and that this work has not formed the basis for the award of
any degree, diploma, associate-ship, fellowship or other titles in
this University or any other University or Institution of higher learning.
Place: Salem
Date: Signature of the Supervisor
iii
ABSTRACT
Many techniques have been proposed by the researchers earlier for
the identification of palm vein for authentication purpose. A set of
methods for the identification and authentication of Palm Veins ( PVs)
which uses various components of the PV images have been proposed for
better accuracy of identification and extraction. Also these methods have
to reduce the identification time with large number of input palm images. It
uses various geometric, wavelet features for the extraction of features of
PV and for the classification of PV used support vector machines which
produces high accuracy in classification.
In recent times, biometrics such as PVs, finger prints, face and iris
recognition have been extensively used in many employments together with
entry admission management, human being authentication for computers,
online banking, ATM‘s and foreign Transaction managements. PV
identification uses the exclusive prototypes of PVs to recognize the persons
at a sky-scraping stage of accuracy. This thesis offers a novel algorithm for
PV identification.
iv
A multi variant volumetric measure to perform palm vein
recognition is proposed. The method normalizes the image by resizing the
image and applies wavelet transform to increase the signal levels. The
transformed image is used to generate number of integral image and for
each integral image a set of Junction points and their coordinates are
identified. The identified features are presented as PV matrix and using
them, the Junctional volume and special volume to compute the
trustworthy measure of the PV given are computed. This method produces
efficient results in the false acceptance rate by reducing it. Also it improves
the accuracy of palm vein identification and authentication. This method
reduces the overall time complexity which is higher in other approaches.
A multi-level dorsal-deep Vein Pattern (VP) based PV recognition
approach is proposed. The method removes the noise and performs
histogram equalization to enhance the image. The enhanced image is
applied with wavelet analysis and splits the higher order and lower order
VP. Generated two different images are split into sub sample images and
their junction points are identified. Identified junction point matrix is used
to compute the dorsal depth and deep vein depth measures to compute the
cumulative weight. Based on cumulative weight an average distance
v
measure is computed to identify the person based on some threshold
value. The proposed method has produced efficient results and reduces
the false ratio and time complexity.
The two proposed approaches for the development of PV
authentication technology have been tested with different number of
classes and samples and produced efficient results in all the factors of
quality of PV recognition and authentication. Experimental results shows
the comparison of PV authentication accuracy produced by different
methods and it shows the proposed methods have produced 96%
accuracy which is better than the other methods at different number of
classes and samples.
vi
ACKNOWLEDGEMENT
I thank God Almighty who has been showering his blessings on
me bestowed strength, knowledge and courage all these days.
I express my sincere gratitude to our Founder of
Vinayaka Missions University, Dr. A. Shanmugasundaram and
I am grateful to respectable Madam Founder Chairman
Mrs. Annapoorani Shanmugasundaram for constant support.
I convey my sincere gratitude to our Chancellor Dr. A. S.
Ganesan and Vice - Chairman Dato Sri‘ Dr. S. Sharavanan, for
permitting me to do this research at this great institution V.M.K.V
Engineering College.
I would like to convey my thanks and gratitude to my guide and
philosopher, Dr. A. Nagappan, Principal, V.M.K.V Engineering College,
Salem, for having guided me in every aspect to complete the research
and thesis. I learned a lot from him. His positive attitude energy and
ability always motivated me to perform. His advice which I always
remember is ―Learn from Experience and Improve Your Work‖.
vii
This advice is always helped me not only to this research but also in
other aspects of my life.
My special thanks to our Vice Presidents Mr. J. Sathish Kumar
and Mr. N. V. Chandrasekar, Vice Chancellor Dr. V. R. Rajendran,
Registrar Dr. Y. Abraham and Dean (Research) Dr. K. Rajendran, of
Vinayaka Missions University, Salem, and to my colleagues and well
wishers who have helped me in one way or other in doing this research.
I would like to thank for CIE Biometrics for providing PUT Vein
Database, without that analysis on vein pattern would not be possible.
Last but not the least, I thank my parents, my wife and children
who were supporting me day in and day out during the course of my
research.
(S.SHARAVANAN)
TABLE OF CONTENTS
CHAPTER
NO.
TITLE PAGE
NO.
ABSTRACT iii
LIST OF TABLES
xiii
LIST OF FIGURES
xiv
LIST OF ABBREVIATIONS
xvii
1
INTRODUCTION
1
1.1 Biometrics and Palm Prints
1
1.2 Need for Palm Print Technology
8
1.3 Biometrics Based Palm Print Verification Process
9
1.4 Operation Modes of Biometric System
11
1.5 Advantages of Palm Print Biometrics
12
1.6 Disadvantages of Palm print Biometrics
14
1.7 PV Patterns
14
1.8 PV Authentication Technology
15
1.9 Principles of Vascular Pattern Authentication
15
1.10 Applications of Biometric Systems
17
1.11 Authentication With PV Images
22
1.12 Details of Technology 23
1.13 PV Acquisition Methods
24
1.14 Organization of The Thesis
25
1.15 Objective
26
2
LITERATURE SURVEY
28
3
PALM VEIN RECOGNITION SYSTEM USING
LOCAL BINARY PATTERN AND GABOR
FILTER USING CLAHE BASED CONTRAST
ENHANCEMENT METHOD
54
3.1 Introduction
54
3.2 Previous Research
55
3.3 Block Diagram
57
3.4 Methodologies
57
3.5 Input, Selected Region and Results of
Combine
Features 58
3.6 Architecture of PV Recognition 75
3.7 Junction Point (JP) Detection 78
3.8 Cross Correlation on Join Point Extraction 78
3.9 Limiting The PV Region 79
3.10 Extending and Sub-Sampling The Contained
Region 80
3.11 Extracting The PV Code By Using the Local
Binary Pattern 81
3.12 Matching The Extracted Codes With Enrolled
ones 83
3.13 Extracting Wavelet Transformed Feature:
Global Features 84
3.14 SVM Classification 86
3.15 Experimental Results 87
3.16 Summary 88
4 MULTI-VARIANT VOLUMETRIC MEASURE
ON UPPER EXTREMITYVP BASED PV
RECOGNITION USING WAVELET
TRANSFORM 91
4.1 Introduction 91
4.2 Methods Explored 94
4.3 Overview of Multi-Variant Volumetric Approach 99
4.4 Normalization 100
4.5 Wavelet Transform on Input Image 101
4.6 Canny Edge Detection
102
4.7 Integral Image Generation
102
4.8 Feature Extraction
103
4.9 Junction Point Identification
103
4.10 Junctional Volume Computation
110
4.11 Algorithm of Junctional Volume Computation
110
4.12 Spacial Volume Computation
111
4.13 Trustworthy Measure Computation
113
4.14 Summary
114
5
MULTI LEVEL DORSAL-DEEP VP BASED
PV AUTHENTICATION USING WAVELET
TRANSFORM
115
5.1 Introduction
115
5.2 Overview of Dorsal-Deep VP Based Approach
120
5.3 Noise Removal
123
5.4 Histogram Equalization
124
5.5 Wavelet Analysis
125
5.6 Sub-Sampling Image Generation
127
5.7 Junction Point Computation 127
5.8 Dorsal Depth Measure
129
5.9 Deep Vein Depth Measure
131
5.10 PV Recognition
132
5.11 Summary
134
6
RESULTS AND DISCUSSION
135
6.1 Multi - Variant Volumetric Measure on Upper
Extremity VP Based PV Recognition Using
Wavelet Transform 136
6.2 Multi - Level Dorsal - Deep VP Based PV
authentication Using Wavelet Transform 152
6.3 Comparative Analysis 163
7 CONCLUSION AND FUTURE WORK 165
8 REFERENCES 169
9 LIST OF PUBLICATIONS 189
LIST OF TABLES
TABLE NO.
Table 2.1
TITLE
Analysis of Various Techniques
PAGE NO.
53
Table 3.1
The Accuracy Rate of PV Images
88
Table 4.1
Displays the Values of Junction Point Matrix
109
Table 6.1
Details of Data Set Being Used
135
Table 6.2
Comparison of Resilience, Rotation and Noise
152
xiii
xiv
LIST OF FIGURES
FIGURE NO.
Figure 1.1
TITLE
Block Diagram of Biometric Verification System
PAGE NO.
10
Figure 1.2
PV Patterns
16
Figure 1.3
ATM with PV Recognition System
18
Figure 1.4
ATM with Small PV Authentication System
19
Figure 1.5 PV across Control Unit 21
Figure 3.1
Block Diagram of Gabor Filter and Local Binary
Pattern
57
Figure 3.2
Examples of Input PV Region
58
Figure 3.3
Examples of Selected PV Region
59
Figure 3.4
VP
60
Figure 3.5
Curvelet Decomposition Pattern
61
Figure 3.6
Gabor Texture Representation Regions
62
Figure 3.7
LBP Texture Representations
63
Figure 3.8
LBP and Gabor Performance
65
Figure 3.9
Individual Samples of Recognition
67
Figure 3.10
Samples Vary with Recognition Percentage – Set 1
68
Figure 3.11
Samples Vary with Recognition Percentage – Set 2
69
Figure 3.12
Comparisons of PCA and Gabor
70
xv
Figure 3.13 Comparisons of PCA and LBP 71
Figure 3.14 Comparisons of PCA and Gabor, LBP 72
Figure 3.15
Comparisons of PCA and Gabor, LBP With
Version Number of Samples
73
Figure 3.16
Comparison of Other Methods with proposed
74
Figure 3.17 Architecture of PV Recognition 77
Figure 3.18 Examples of Localizing the PV Region with Masks 80
Figure 3.19 Stretched Images of Figure 81
Figure 3.20
The LBP Operator
82
Figure 3.21
Graphical Representation of Accuracy Performance
89
Figure 3.22
Graphical Representation of Processing Time
90
Figure 4.1
Palm VP of Hand
92
Figure 4.2
Abstract VP of Human Hand
93
Figure 4.3
Proposed System Architecture-I
100
Figure 4.4
Block Diagram of Normalization
101
Figure 5.1
Displays the Abstract VP
116
Figure 5.2
Proposed System Architecture-II
122
Figure 6.1
Snapshot of Input PV Image Selected
137
Figure 6.2
Snapshot of Boundary Marked
138
xvi
Figure 6.3 Rotated Snapshot of PV Image and the
Region Marked to be Extracted
139
Figure 6.4
Snapshot of Extracted Region of Interest
140
Figure 6.5 Snapshot of Noise Removed Image 141
Figure 6.6
Snapshot After Background Removal
142
Figure 6.7
Snapshot of Normalized PV Image
143
Figure 6.8
Snapshot of Skeleton Identified Image
144
Figure 6.9
Snapshot of Identified Junction Points in the Image
145
Figure 6.10
Snapshot of PV Image Matched
146
Figure 6.11
Snapshot of Step by Step Result of Proposed
147
Figure 6.12
Snapshot of Input Image Selected for PV
Recognition
153
Figure 6.13
Snapshot of Region Extracted
154
Figure 6.14
Snapshot of Histogram Equalized ROI Image
155
Figure 6.15
Snapshot of Background Subtraction
156
Figure 6.16
Snapshot of Normalized Image
157
Figure 6.17
Snapshot of Junction Point Identified Image
158
Figure 6.18
Snapshot of Identified PV Image
159
xvii
LIST OF ABBREVIATIONS
ANN Artificial Neural Network
ASIFT Affine-SIFT
ED Euclidean Distance
EER Equal Error Rate
HD Hamming Distance
JP Junction Point
LBP Local Binary Pattern
LDP Local Derivative Pattern
NBI Normalized Back scattered Intensity
NIR Near InfraRed
PCA Principal Component Analysis
PV Palm Vein
ROI Region of Interest
SIFT Scale Invariant Feature
SVM Support Vector Machine
SURF Speeded-Up Robust Features
VP Vein Pattern
1
CHAPTER 1
INTRODUCTION
1.1 Biometrics and Palm Prints
Today, in our daily life, we are often being asked for verification of
our identity. Normally, this is done through the use of passwords when
pursuing activities like domain accesses, single sign-on, application logon
etc. In the process, the role of personal identification and verification
becomes increasingly important in our society. With the onslaught of
improved forgery and identity methods of impersonation, correct
authentication in previous ways is not sufficient. Therefore, new ways of
efficiently proving the authenticity of an identity at a low cost are heavily
needed. Various ways of approach have been explored to provide a solution
and biometric-based identification is proved to be an accurate and efficient
answer to the problem. Biometrics has been an emerging area of research in
the recent years and is devoted to identification of individuals using physical
traits, such as few based on hand geometry, iris, face recognition, finger
prints, or voices. As unauthorized users are not able to display the same
unique physical properties to have a positive authentication, reliability will
be ensured. This is much better than the current methods of using
2
passwords, tokens or personal identification number (PINs). At the same
time it provides a cost effective convenient way of having nothing to carry
or remember.
Identity management becomes more sophisticated due to the
development of digital processing techniques. Whatever be the
authentication system there is a presence of digital verification process
exists and that may be using any of the human anatomical part like nose,
eyes, palms, etc. The palm print is one among them which is becoming
more popular now a days. There are many researches going on palm print
recognition for various requirements. The digital image of palm print shows
the internal structure of nerves in human palms which is unique for each
human and how it could be used to identify a person is the vital problem.
Computer-based personal identification, also can be said as biometrics
computing began in 1970s. At that time, ‗Identity‘, the first commercial
system was developed, which measured the shape of a hand and focused
particularly on finger length. In the meanwhile, finger print-based automatic
checking systems were widely used in enforcement of law. Retina based
systems and iris-based systems were introduced in the mid-1980s. Today's
speaker identification has its root in the technological achievements of the
3
1970s; while signature identification and facial recognition are relative
newcomers to the industry.
In the ubiquitous network society, Individuals can also easily access
their information anytime and anywhere can be obtained and people are
also faced with the risk that others can easily access the same
information anytime and anywhere. Due to this risk, personal identification
methodology, which can differentiate among registered users and
imposters, is of grave importance.
Currently, passwords, Personal Identification Numbers (4-digit PIN
numbers) or identification cards are used for personal identification.
However, there is every likelihood that cards can be stolen or forgotten,
guessing passwords and guessing numbers are possible. Biometric
authentication technology is used to solve these problems, which identifies
people based on their unique biological information and it deserves
attention. In biometric authentication, an account holder‘s behaviors or
body characteristics are registered in a database and then compared with
others who may try to access that account to see if the attempt is legitimate.
The term biometrics refers to a scientific discipline involving
automatic methods for recognizing (verifying or identifying) people based
4
on their physical and/or behavioral characteristics. Many biometric systems,
exploiting these methods to establish identity, have previously been
presented in the literature among them, methods which make use of
biometric characteristics such as finger prints, face, voice, iris, retina, hand
geometry, signature or palm prints are the most common. While a
considerable research effort is directed towards the development of fast,
robust, efficient and user-friendly biometric systems, some major problems
that are still need to be tackled before they can be deployed on a larger
scale. One of the major challenges, which is yet to be solved, includes
increasing the performance of biometric systems in terms of recognition.
Towards this goal, a recent trend has emerged such as the employment of
multi-modal biometric systems which establish identity either by
considering several biometric modalities (e.g., the face, the iris, palm
prints, voice etc.) or by combining the recognition results of several
algorithms performed on the same biometric sample. The valid solution
from such approaches for the problem of recognition performance,
unfortunately user-convenience gets decreased, as it requires a much greater
effort from the user to operate the system or it increases the time needed to
process a single user. Hence the remedy is worse. From this point of view,
other solutions capable of increasing the recognition rates and not
5
influencing the convenience of using the biometric systems should be found
out. One possibility of increasing the recognition performance is to closely
examine the feature normalization techniques, which form the criteria of
decreasing the error rates of biometric systems, but have so far been
largely omitted in most research papers on the subject of biometrics.
Generally, only a sentence or two is devoted to the employed normalization
technique, even though representation of feature normalization is crucial
step in the design of a biometric system. Always feature normalization
techniques have a great impact on the procedure of constructing user
templates (or models), i.e., mathematical representations of the feature
vectors extracted from several measurements of the biometric
characteristic (e.g., palm prints) acquired during the enrollment stage, and
consequently on how user-specific biometric characteristics are modeled.
They represent a faster and more efficient way of boosting the
recognition performance of biometric systems which does not significantly
increase the processing time of a user.
These days many applications of biometrics are being used or
considered worldwide. Most of the applications are still at the early stages
of testing process, and end users find it as an optional. Any circumstance
6
that allows an interaction between man and machine is capable of
incorporating biometrics. Such situations may fall into a specific range
of application areas such as computer desktops, laptops, wired & wireless
networks, online banking and immigration, enforcement of law,
telecommunication synchronous and asynchronous networks. Fraud is
an ever-increasing problem and security is becoming a must in many walks
of life. Though research on the issues of finger print identification and
speech recognition have drawn considerable attention over the last 25
years and recently issues on face recognition and iris-based verification
have been studied extensively there are still some limitations to the
existing applications. Some finger prints of few peoples get worn away
due to the hand-work and some are born with unclear finger prints. The
existing iris-based identification system has not been proved to be
adaptive to eastern people who have quite different iris patterns from
those of people from western. voice based identification systems and
Face identification systems are less accurate and easy to be imitated. Efforts
on improving the present personal identification methods are to continue
and meanwhile newmethods are under investigation.
7
The palm print uses the similar set of characteristics used for finger
print recognition. Characteristics like ridge flow, ridge characteristics and
ridge structure of the raised portion of the epidermis are adapted. The data
represented by these friction ridge impressions either originated from the
same source or could not be made by the same source.
Palm print is based on ridges, principal lines and wrinkles on the
surface of the palm. A palm print refers to an image acquired of the palm
region of the hand. It can be either an online image (i.e. taken by a
scanner, or CCD) or offline image where the image is taken with ink and
paper.
The palm itself consists of principal lines, wrinkles (secondary lines)
and epidermal ridges. It varies from a finger print in that it also contains
other information such as indents, texture and identification marks which
can be used when comparing one palm to another.
Palm prints can be used for scientific tests or techniques used in
connection with the detection of crime or commercial applications. Palm
prints are normally found at crime scenes as the result of the offender's
gloves slipping during the time of crime, and thus exposing part of the
unprotected hand.
8
1.2 Need for Palm Print Technology
Biometrics has been an emerging field of research in the recent years
and is devoted to identification of individuals using physical traits, such as
those based on iris or retinal scanniridgesng, face recognition, finger
prints, or voices. As unauthorized users are not able to display the same
unique physical properties to have a positive authentication, reliability will
be ensured. Palm print is preferred compared to other methods such as
finger print or iris because it is always identical and can be easily
captured using low resolution devices as well as contains additional
features such as principal lines. Iris input devices are expensive and the
method is intrusive as people might fear of adverse effects on their
eyes. Finger print identification requires high resolution capturing
devices and may not be suitable for all as some may be finger
deficient. Palm print is therefore suitable for everyone and it is also non-
intrusive as it does not require any personal information of the user. Palm
print images are captured by acquisition module and are fed into recognition
module for authentication.
Compared with face recognition palm prints are hardly affected by
age and accessories.
9
Compared with finger print identification images of palm print
contain more information and need only low resolution image capturing
devices which reduce the cost of the system.
Compared with iris recognition the palm print images can be
captured without intrusiveness as people might fear of adverse effects on
their eyes and cost effective. Hence it has become such an important and
rapidly developing biometrics technology over the last ten years. Limited
work has been submitted on palm print identification and validation, without
being affected by the importance of palm print features. The functions of
the system is done by projecting palm print images onto a feature space
that spans the significant variations among known images.
1.3 Biometric Based Palm Print Verification Process
Biometric system is basically a pattern recognition system which
identifies a person using psychological or emphasizing behavioral metrics.
The characteristics such as 3D hand geometry, finger print and palm print
are read into system using some scanners and sensors and return a result.
Any kind of biometric system (Figure 1.1) has four stages named
1. Data Acquisition
2. Preprocessing
10
3. Feature Extraction
4. Feature matching
Figure 1.1 Block Diagram of Biometric Verification System
Data acquisition
The first stage of biometric verification process where the input
signals or images are gathered using input devices such as scanners. The
quality of signal given as input or image plays a vital role because the
quality of result depends on the quality of input signals or images.
Preprocessing
At this stage the signal quality and image is improved using various
11
preprocessing stages like filtering, normalization, rotation, segmentation and
noise removal.
Filtering: This is the process of selection of pixels from set of pixels
and the selection of signal or pixel depends on the value of pixel or signal.
Noise removal: This is a procedure of avoiding incomplete signals
and pixels from further stage of processing.
Feature Extraction: The process of extracting stable properties of
intra- class difference and high intra-class difference. They are used to build
the template of the data base.
Feature matching: Is a matching procedure to compute matching
score with the featured template and master template.
1.4 Operation Modes of Biometric System
Any kind of biometric system has three operating modes:
Enrollment
This combines the first three stages of the biometric verification
systemnamely (data acquisition / data developing skills, preprocessing, and
feature extraction). Any user has to be enrolled before verification
into the system by data acquisition and the features have to be extracted
12
and then stored into the system.
Identification
This tells the matching process of the biometric system. It works to
find out the user with the biometric features obtained from the user and
match with other biometric templates available in the system. It initiates
identification process without knowing the identity of the user.
Verification
The verification process is done when an identification process goes
successfully and then there is searching the record to identify the person
about name, ID card and other attributes.
1.5 Advantages of Palm Print Biometrics
Since the palm area is much larger so that more distinctive features
can be captured and compared to finger prints. This makes it much more
suitable in the process of identification systems than finger prints.
Advantages of using the palm
In addition to the palm, vein authentication can be done using the
vascular pattern on the back of the hand or a finger. However, the pattern in
the PV is most complex and covers the widest area. Due to the palm has
13
no hair, it is easier to photograph its vascular pattern. The palm also has no
sufficient variations in skin color compared with fingers or the back of the
hand, where the color can darken in specific areas.
There are two methods of photographing veins: reflection and
transmission. Fujitsu implements the reflection method. The reflection
process illuminates the palm and photographs the light that is reflected back
from the palm, while photographs light of the transmission method passes
straight through the hand. Both methods capture the nearby infrared light
given off by the region used for identification after diffusion through the
hand. Such an important difference between the reflection method and
transmission method is how they respond to changes in the hand‘s light
transmittance. When the body gets cool due to a low ambient
temperature, the blood vessels in particular capillaries decreasing the flow
of blood throughout the body. This suits up the hand‘s light transmittance,
so light passes through it in much more easier way. If the transmittance is on
the higher end, the hand can become organic molecule with light and light
can easily pass through the hand. In the transmission process, this yields
results in a lighter, less-contrasted image in which vessels are difficult to
see. However, a higher level of light transmittance does not significantly
14
affect the level or contrast of the reflected light. Therefore as a result,
with the reflection method, the vessels can much more easily be seen even
when the hand/body is cool.
1.6 Disadvantages of Palm print Biometrics
The palm print scanners are generally bigger in size and expensive
since they need to capture a larger area than the finger prints scanners.
1.7 PV Patterns
Blood veins are formed during the first eight weeks of gestation in a
chaotic manner, influenced by the environment like mother‘s womb. This is
why VP is identical to each individual, even twins. Vein growth is
associated with a person‘s skeleton, and while capillary arrangement
continue to grow and change, vascular patterns are formed during birth
and do not change over the course of one‘s lifetime.
To scan the veins, an individual‘s hand is placed on the hand guide
(the plastic casing of the scanner device) and the VP is captured by lighting
the hand with near-infrared light. Veins consist of deoxidized
hemoglobin, an iron-containing coloring matter (pigment) in the blood
that carries oxygen throughout the body. These pigments absorb the near IR
light and reduce the reflection rate causing the veins to appear as a black
15
pattern. An every individual‘s scanned PV data (biometric template) is
encrypted for a protection and registered along with the other details in
his/her profile as a reference for future comparison.
1.8 PV authentication Technology
PV authentication is performed according to the comparison
performed between various patterns of human PV. The PVs are the lines
appear in the palm image with the blue lines and such patterns are extracted
and stored in the PV data base. Vascular patterns are generis to each and
every individual, according to Fujitsu research — even identical twins have
different patterns. And since the vascular patterns on the body exist inside,
they cannot be duplicated by means of photography, voice recording or
pattern of finger prints, thereby making this procedure of biometric
authentication more secure than others.
1.9 Principles of Vascular Pattern Authentication
Hemoglobin in the blood is oxygenated in the lungs and carries
oxygen to the tissues of the body through the arteries. After it gets release
its oxygen to the tissues, the deoxidized hemoglobin backs to the heart
through the veins. These two kinds of hemoglobin have distant rates of
absorbency.
16
Deoxidized hemoglobin absorbs light at a wavelength of about 760
nm in the near-infrared region. When the palm gets illuminated with near IR
light, unlikely images can be seen by the human‘s eye, the deoxidized
hemoglobin in the PVs absorb light, hence reflection rate gets reduced and
causing the veins to appear as a black pattern, based on this principle the
region used for authentication on vein is photographed with near-infrared.
Using image processing [Figure 1.2], light and the VP is extracted
and registered. The VP of the person being authenticated is then verified
against the preregistered pattern.
Figure 1.2 PV Patterns
17
1.10 Applications of Biometric Systems
In spite of product development for financial solutions financial
damage is caused by fraudulent withdrawals of money using identity
spoofing with fake bankcards has been fast increasing in last years, and this
has emerged as a significant problem in society. Therefore, rapid increase in
the number of lawsuits filed by victims of identity theft against financial
institutions for their failure to control information to be used only for
personal identification. ―Protection of Personal Information legal Act‖ force
into effect in Japan on 1st May 2005.
Financial institutions have been focusing on biometric authentication
together with IC (smart) cards as a way to reinforce the security of personal
identification. Vein authentication always providing two kinds of systems
for financial solutions, depending on the registered VPs are stored. In one
method, the VPs are stored on the server of a client-server system. The
benefits of this system are that it provides an integrated capability for
managing VPs and comparison processing. In the other type, a user‘s VP is
stored on an integrated circuit card, which is beneficial because users can
control access to their own VP. Suruga Bank uses the server type for their
18
financial solutions, and Tokyo-Mitsubishi is the bank uses the integrated
circuit card system.
Figure 1.3 ATM with PV Recognition System
In July 2004, to ensure customer security, Suruga Bank introduced its
―Bio-Security Deposit‖ — the world‘s first financial service to use Palm
Secure (Figure 1.3). These kinds of services provide high security for
customers using vein authentication, does not require a verification proof
like bankcard or passbook which are used to prevent withdrawals from
branches other than the registered branch and ATMs, hence as a result
minimizing the risk of fraudulent withdrawals. To open a Deposit
account with Bio-Security features, customers go to a bank and have their
PVs photographed at the counter. In order to make sure about the secure
data management, the PV data is stored only on the vein database server at
the branch office where the account is opened.
19
Figure 1.4 ATM with Small PV authentication System
In October 2004, The Bank of Tokyo-Mitsubishi4 launched its
―Super-Integrated Circuit Card Tokyo Mitsubishi VISA.‖ These types of
cards always have the functions of a bankcard, credit card, electronic money
and PV authentication. From a technical prospect and user-friendly point of
view, Tokyo - Mitsubishi Bank narrowed the biometric authentication
methods suitable for financial transactions to PVs, finger veins and finger
prints. The bank then mailed a feedback form to 1,000 customers
and surveyed an additional 1,000 (Thousand) customers who used the
devices in their branches. At the final stage, the bank decided to employ
Palm Secure because the technology was supported by the largest number
of people in the questionnaire. The Super-Integrated Circuit Card contains
the customer‘s PV data and vein authentication algorithms combines
and performs vein authentication by itself. This system is beneficial
because the customer‘s information is not stored at the bank.When a
20
customer applies for a Super-Integrated Circuit card, the banker sends
the card to address of the customer‘s home. To activate the PV
authentication function, the customer brings the verification card and his or
her passbook and seal to the bank counter, where the customer‘s
required vein information is registered on the card. After registration
process, the customer can make transactions at that branch‘s counter
and any ATM (Figure 1.4) using PV authentication and a matching PIN
number.
The Hiroshima Bank started this type of service in date of April 2005,
followed by The Bank of Ikeda6 in date of June 2005. Other
financial institutions, including The Nanto Bank, planned and organized
to start similar services during fiscal 2005.
In 2006, Fujitsu reduced the Palm Secure sensor to 1/4 of its current
size for its next generation product. By using a sensor on existing ATMs,
there will be room or place on the operating panel for a sensor for Felicia
mobiles, a 10-key pad that meets the Data Encryption Standard (DES), as
well as other devices including electronic calculator. The downsized sensor
can also be installed on ATMs in convenience stores.
21
In addition to product development for financial solutions, Fujitsu has
initiated to develop product applications for the general market. Two
products moving faster are in great demand on the general market place.
One product is for a physical access control unit that uses Palm Secure to
protect entrances and exits, and the other product is nothing but a logical
access control unit that uses Palm Secure to protect input and output of
electronic data.
Palm Secure units are used to control access to places containing
systems or machines that manage personal or other confidential and more
secured information, such as machine rooms in companies and outsourcing
centers where important customer data is kept.
Figure 1.5 PV across Control Unit
Due to increasing concerns about security, some commercial sectors
and homes have started using this system to enhance security and safety in a
day to day life. Considering both of these applications, the combined
22
form of the following advantages provides the optimum system: a hygienic
and contactless unit ideal for use in public places, simple user-friendly
operation that requires the user to simply hold a palm over the sensor, and
an authentication mechanism that provides impersonation difficult. The PV
authentication login unit controls access to electronically stored information
(Figure 1.5). When considering the units for financial solutions, there are
two types as follows: a server type and an Integrated Circuit card type.
Becausethe Palm Secure login unit can also be used for authentication
using conventional IDs and passwords, existing operating systems and
available applications can continue to be used. It is also possible to
develop the unit into an existing application to enhance operability. In
the initial stage of introduction, the units are having limitations like
areas of businesses handling personal information that came under the
―Protection of Personal Information Act‖ enforced in the date of April 2005.
However, usage of the units is now expanding to leading-edge businesses
that handle confidential information.
1.11 Authentication with PV Images
Unlike the previous vein feature based authentication mechanism, the
vein code based authentication mechanism extracts the feature codes from
23
palm vein imagery which is represented in binary way. The binarized data
set improves the efficiency of authentication as well as increases the speed
of authentication. From the single piece of PV image there are number of
feature codes can be generated and converted in to binary form. The
generated binary form data can be applied to various problems and can
provide uninterrupted service to many areas. The modern technology
extends the application and scope of PV authentication mechanism which
enables the possibility of using biometric authentication mechanism in a
dense organization.
The application of PV feature based authentication mechanism is
growing in day by day which uses biometric features and the feature is very
much unique for any person. This improves the essential secure storage of
bio features which can be shared between many application.
1.12 Details of Technology
PV image normalization technique
The PV image has to be normalized before being used to perform PV
authentication. The normalization method must be more efficient so that the
features of PV could be maintained. To perform such efficient
normalization, the Fujitsu Laboratories has developed an efficient method
24
which uses contour information to maintain the sh ape and position in PV
images. The image captured by the device attached is verified for the
position and shape and the method removes the distortion from the image
obtained.
Feature code extraction technology
The method uses a different size of feature code which is in the size of
2048 bits, a 2 byte style. The method first generates a sectional image which
fixed size and splits the image into number of regions. For each region the
method extract the features and according to the amount of information
present in the regional image, a 2 byte information or code is framed. The
generation of 2 byte information is done by compressing the information
present in the region. The region based approach enables the
identification of micro changes in the VPs which can be introduced at
different shapes and positions.
1.13 PV Acquisition Methods
There are many ways to snapshoot the PV but each differs with the
accuracy of the vein image.
25
High-Speed Image-Capture Technology for PV Biometric
authentication
Fujitsu Lab developed a prototype authentication device that
employs a high-speed shutter to capture images of the PVs without
blurring even when the palm is in motion, in contrast to the previous
version which captured images when the palm was suspended above the
sensor.
An improved PV identification based on thermal PV image
The infrared PV image is captured using the infrared waves and the
image is stored for processing.
1.14 Organization of the Thesis
This thesis is organized into seven chapters. Chapter 1 gives the
introduction to thesis with Biometrics concepts and palm prints for the
research work. Chapter 2 describes the literature survey related to
palm prints, Algorithms and Methods. Chapter 3 deals with the PV
recognition system using local binary pattern and Gabor filter using
Clahe based contrast enhancement method. In Chapter 4, the work is
Multi – Variant Volumetric Measure on Upper Extremity VP Based PV
Recognition Using Wavelet Transform.
26
Chapter 5 focuses on the Multi-Level Dorsal-Deep VP Based
PV authentication Using Wavelet Transform while Chapter 6 is
devoted to Results and Discussion and Chapter 7 forms the conclusion and
future work envisages respectively.
1.15 Objectives
Biometrics such as PVs, palm prints, face recognition and iris
identification have been extensively used in a lot of employments together
with entry admission management, human being authentication for
computers, online banking, ATMs and foreign Transaction
managements. PV identification uses the exclusive prototypes of PVs to
recognize the persons at a sky-scraping stage of precision. This
work offers two approaches for the development of PV authentication
technology namely A multi variant volumetric measure to perform PV
recognition and A multi- level dorsal-deep VP based PV recognition
approach Both the methods show very high accuracy and also less
processing time.
1. To study and apply appropriate image segmentation technique on VP.
2. To measure VP based PV recognition using wavelet transform.
27
3. To identify features present in PV matrix which is used to compute the
Junctional volume and special volume to find the trustworthy measure of
the PV given.
4. To achieve a better accuracy and low False Acceptance Rate (FAR).
5. To achieve less Processing Time compared to existing methods.
28
CHAPTER 2
LITERATURE SURVEY
The extended research of palm print has been done in many years and
there has been various methodologies have been proposed for the
identification of palm print of a particular person in this era. Some of the
unique methodologies here and their effectiveness and default with many
characteristics are explored here.
Junichi Hashimoto, 2006, discussed VPs based biometric
authentication approach for biometric authentication. Secured Smart Card
Using PV Biometric On-Card-Process [1], discuss the PV biometric
system and its compatibility in financial sector, software design for on-card-
processing solution based on Java virtual machine.
In both the paper the solution is stimulated and the result obtained
and that was tested on PC login application. This increases the tampering of
forgery and increases the quality of authentication. The security level of PV
Biometric On-Card-Process [1], is highly reliable since the FRR and FAR
is very low compared to other biometric systems.
A PCA based PV authentication system is presented in [3], which
uses the Princple component analysis method to perform feature extraction
29
and Yuhang Ding, Dayan Zhuang and Kejun Wang, July 2005[4], analyzed
the difficulty of hand vein recognition and propose a thresol segmentation
with thinning approach.
To capture the palm vein image the infrared camera is used and by
using the PCA approach the extracted features are converted into feature
vector. Both the papers using the highest information of varying size, the
pattern matching is performed to find out the best match from the data base
to perform authentication. The method extracts the edge and junction points
and then performs pattern matching to compute the distance. Based on
computed distance the method performs biometric authentication.
In combination of [3] and [4], there exist Shi Zhao, Yiding Wang
and Yunhong Wang, proposed [5] , uses a hand dorsa to extract the edge
features of palm vein to perform biometric authentication. The method
replaces the necessary of using high quality devices and reduces the cost of
biometric authentication.
In combination with above study Yi-Bo Zhang et al [16] , discusses a
palm vein authentication mechanism which also captures the palm vein
image through the infrared palm image capturing device and then it
identifies the region of interest. Once the region is being identified then the
method extracts the palm vein pattern which is obtained by applying multi
30
level filtering technique. Finally the extracted pattern is performed with
patten matching to perform biometric authentication.
Masaki Watanabe, Toshio Endoh, MoritoShiohara, and Shigeru [6]
and Shani Sarkar et al [12], performs a detailed inspection on the palm vein
authentication mechanisms presented earlier which uses the vessel pattern
of person hands. The growth of biometric authentication has great influence
on various sectors person identification in banks, markets and more.
Paper [6] have shown a biometric authentication using contactless PV
authentication device that uses blood vessel patterns as a personal
identifying factor. Implementation of these contactless identification
systems enables applications in public places or in environments where
hygiene standards are required, such as in medical applications. In addition,
sufficient consideration was given to individuals who are reluctant to come
into direct contact with publicly used devices.
Proposed Multi-Modal PVs-Face Biometric authentication [9],
presents a multimodal PVs and face biometric verification system which
enhances the quality of biometrtic authentication by extracting palm vein
and facial features.
The method combines both PV and facial features to perform
biometric authentication which utilize different methods like RLM( Run
31
length matrix), GLCM (gray level co-occurrence matrix), SF (Statistical
Features), and Moment Invariants (MIs). As a improment of above [9],
Muhammad Imran Razzak et al [11] is developed and it uses a combined
face and finder model based biometric authentication mechanism.
The method uses the multi level fusion approach to extract the
patterns of face and finger veins. Based on the finger vein patterns and face
features the method computes the similarity to perform biometric
authentication. The method uses fuzzy based method to perform biometric
authentication.
Yingbo Zhou and Ajay Kumar [15] discussed two different palm vein
authentication approach which uses hessian phase details which are
extracted from the human vascular patterns from the palm vein images. In
the second method, they used orientation patterns of palm vein lines based
on radon transform.
Junction Point based person identification [18], is proposed which
uses a Junction Point which is defined as the intersection point of the three
or more line segments and a fast JP detector is proposed. In addition to the
study [18], Palm print texture analysis based on low-resolution images
for personal authentication [25], proposes a new branch of biometric
approach - palm print technology, whereby the lines and points can be
32
extracted from our palm for personal authentication- was proposed several
years ago. Study [18] focuses a new feature extraction method based on
low-resolution palm print images and a 2-D Gabor filter is used to obtain
the texture information and two palm print images are compared in term of
their hamming distance.
Junction Point based person identification [18], make Junction Points
of the palm print and PV line segments associated with their
directions of palm print and PV are computed. Transition number is used to
detect the junction function.
The edge segments are thinned using a morphological operation.
Then centre pixel within a 3×3 neighborhood, which is a junction that is
tested. In this paper, they proposed recognition approach by combining
palm print and PV at the feature level.
A novel feature, Junction Point (JP), which is obtained on the fused
line segments images are proposed. JP features have been verified as a
more compact and accurate representation of palm images.
A performance Evaluation of Shape and Texture based methods for
vein recognition [19], is improved from above [18] on the basis to give fair
comparisons of shape and texture based methods for vein recognition.
The shape of the back of hand contains information that is capable of
33
authenticating the identity of an individual. In this paper, two kinds of shape
matching method are used, which are based on Hausdorff distance and Line
Edge Mapping (LEM) methods. The vein image also contains valuable
texture information, and Gabor wavelet is exploited to extract the
discriminative feature.
The edge methodology is also applied in Person recognition by
fusing palm print and PV images based on Laplacian palm representation
[39], combines both palm print and palm vein features to perform person
recognition. Both palm print and palm ven images are applied through the
edge preserving algorithm and then the contrast of the image is enhanced
through wavelet fusion technique. The extracted features are represented by
locality preserving projection to perform matching process.
Biometric Verification Using Thermal Images of Palm Dosra VPs
[20], discuss a biometric authentication mechanism which uses the palm
vein pattern extracted from the thermal images.
The method does not require any prior knowledge about the objects.
The method uses an the IR camera is incorporated to capture the thermal
images. Once the method captures the input image then the region of
interest is identified and feature vein patterns are extracted to perform palm
vein authentication.
34
The method uses watershed segmentation and multi level filtering to
extract the vein patterns to improve the performance of the biometric
authentication.
Coherence-enhancing Diffusion Filtering‖, International Journal of
Computer Vision [21], present a multi scale method in which a nonlinear
diffusion filter is steered by the so-called interest operator (second-moment
matrix, structure tensor). An m-dimensional formulation of this method is
analyzed with respect scale-space properties.
An efficient scheme is presented which uses a stabilization by a
semi-implicit additive operator splitting (AOS), and the scale-space
behavior of this method is illustrated by applying it to both 2-D and 3-D
images.
Image Enhancement and Desnoising by Complex Diffusion Processes
[22], present an biometric authentication method which uses linear and non
linear methods using real valued diffusion equations. The method proves
that the the method generates secondary smooth derivative.
In [23] Guy Gilboa, NirSochen and Y.Yehoshua Zeevi analyze and
prove some properties of coupled shock and diffusion processes. Finally an
original solution of adding a complex diffusion term to the shock equation
35
is proposed. This new term is used to smooth out noise and indicate
inflection points simultaneously. The imaginary value, which is an
approximated smoothed second derivative scaled by time, is used to control
the process. This results in a robust deblurring process that performs well
also on noisy signals.
In [24] SuleymanMalki, Yu Fuqiang and Lambert Spaanenburg and
Biometric Recognition: Security and Privacy Concerns [26], discusses the
biometric identification which is an important security application that
requires non-intrusive capture and real-time processing.
Security systems based on finger prints and retina patterns have been
widely developed, but can be easily falsified. Recently, identification by
VPs has been suggested as a promising alternative. The method uses the
available feature extraction approach to improve the performance of palm
vein authentication.
The biometrics which offers greater security and convenience than
traditional methods of personal recognition. In some applications,
biometrics can replace or supplement the existing technology. In others, it
is the only viable approach.
Feature Level Fusion of PVs and Signature Biometrics [32],
discusses the traditional biometric systems that based on single
36
biometric usually suffer from problems like imposters' attack or hacking,
unacceptable error rate and low performance. So, the need of using
multimodal biometric system occurred .In this paper, a study of multimodal
PVs and signature identification is presented.
Features of both modalities are extracted by using morphological
operations and Scale Invariant Features Transform (SIFT) algorithm and a
comparison for both methods is developed. Feature level fusion for both
modalities is achieved by using a simple sum rule. Fused features vectors
are subjected to Discrete Cosine Transform (DCT) to reduce their
dimensionalities.
In Vascular Pattern Analysis towards Pervasive PV authentication
[33], infrared images are used to perform biometric authentication which is
sent through three different levels. These infrared images are also discussed
in [18], [19] and [25].
First the image is pass through the vascular pattern marker to perform
marking and then feature is extractd using the VPEA (vascular pattern
extractor algorithm). Third the extracted feature is passed through vascular
pattern thinning algorithm.
The final image is indexed to the data set to perform biometric
authentication. Infrared camera is also utilized in PV extraction and
37
matching for personal authentication [35], performs palm vein
authentication based on the image captured through the infrared camera.
First theimage is smoothed and then the features are extracted by
identifying the region of interest. The feature extraction is performed by the
multi level scale filtering and finally matching is performed.
Human Identification Using Palm-Vein Images [34], discuss
variety of approaches for biometric palm vein authentication problem. The
proposed approach attempts to more effectively accommodate the potential
deformations, rotational and translational changes by encoding the
orientation preserving features and utilizing a novel region-based matching
scheme.
Curve let-based PV biometric recognition [36], presents a novel
personal recognition system utilizing palm VPs and a novel technique to
analyze these VPs. The technique utilizes the curve let transform to extract
features from VPs to facilitate recognition.
This technique provides optimally sparse representations of objects
along the edges. Principal component analysis (PCA) is applied on curve
let-decomposed images for dimensionality reduction. A simple distance-
based classifier, such as the nearest-neighbor (NN) classifier, is employed.
PV Verification System based on SIFT matching [37], presents
38
a novel personal recognition system utilizing palm VPs and a novel
technique to analyze these VPs. The technique utilizes the curve let
transform to extract features from VPs to facilitate recognition.
This technique provides optimally sparse representations of objects
along the edges. Principal component analysis (PCA) is applied on curve
let-decomposed images for dimensionality reduction. A simple distance-
based classifier, such as the nearest-neighbor (NN) classifier, is employed.
The experiments are performed using the PV database. Experimental
results show that the algorithm reaches a recognition accuracy of 99.6% on
the database of 500 distinct subjects.
Multispectral palm image fusion for accurate contact-free palm print
recognition [40], propose to improve the verification performance of a
contract-free palm print recognition system by means of feature- level
image registration and pixel-level fusion of multi-spectral palm images. The
method involves image acquisition via a dedicated device under contact-
free and multi-spectral environment, preprocessing to locate region of
interest (ROI) from each individual hand images, feature-level registration
to align ROIs from different spectral images in one sequence and fusion to
combine images from multiple spectra. The advantages of the
proposed method include better hygiene and higher verification
39
performance.
Biometric identification is an important security application that
requires non-intrusive capture and real-time processing. Security systems
based on finger prints and retina patterns have been widely developed, but
can be easily falsified. Recently, identification by VPs has been suggested
as a promising alternative.
In Vein Feature Extraction Using DT-CNNs [41], an existing
feature extraction algorithm that has been developed for finger print
recognition is adapted for vein recognition.
An improved PV recognition system using multimodal features and
neural network classifier has been developed and presented in an Enhanced
PV Recognition System Using Multi-level Fusion of Multimodal Features
and Adaptive Resonance Theory [42].
Biometric Cryptosystem Involving Two Traits and PV as Key [43],
proposes a scheme which involves an idea of including three biometric
traits of a person where in the sense even if one fails the other trait could be
utilized for verification or identity.
Moreover the concept of cryptosystem is involved, where one of the
biometric trait – the PV itself acts as a key to utilize the stored template
database. The main idea in using one of the biometric traits as a key is
40
that under any circumstance no two PVs match unless it belongs to same
person. It is a valid key which no one can steal or misuse.
Online joint palm print and PV verification [44], discuss a combined
palm print with vein approach and deployed in the web to perform
biometric authentication.
To improve the performance and speed the web based solution is
deployed with the support of dynamic fusion technique which extract the
features of palm print and vein features. The extracted feature is stored in a
data set to perform matching and the method has produced more speed with
precision.
PV Recognition with Local Binary Patterns and Local Derivative
Patterns [45], presents a promising new approach based on local texture
patterns. First, operators and histograms of multi-scale Local Binary
Patterns (LBPs) are investigated in order to identify new
efficient descriptors for palm VPs. Novel higher-order local pattern
descriptors based on Local Derivative Pattern (LDP) histograms are then
investigated for PV description.
Both feature extraction methods are compared and evaluated in the
framework of verification and identification tasks. Extensive experiments
on CASIA Multi-Spectral Palm print Image Database V1.0 (CASIA
41
database) identify the LBP and LDP descriptors which are better adapted to
PV texture. Tests on the CASIA datasets also show that the best adapted
LDP descriptors consistently outperform their LBP counterparts in both PV
verification and identification
Biometric Verification Using Thermal Images of Palm Dosra VPs
[48], uses palm dorsa infrared image as input which is captured using the
infrared camera. In the second stage the method extract the features and the
input captured image is submitted to the web server where the extraction
and matching is performed.
Thermo graphic imaging of the subcutaneous vascular network of the
back of the hand for biometric identification [50], describes research that
has been undertaken by the authors to use the subcutaneous vascular
network (VP) of the back of the hand as a unique personal biometric for
identification.
An outline will be given of a prototype low cost automatic thermo
graphics imaging system which has been developed by the authors to obtain
VPs for positive identification. The paper includes consideration of the
image acquisition, image processing and VP matching strategies. A
summary of experimental results concerning the acceptance and rejection
rates for the system is also provided.
42
Improved VP extracting algorithm and its implementation [51],
proposes an improved VP extracting algorithm which compensates the loss
of VPs in the edge area, gives more enhanced and stabilized VP
information, and shows better performance than the existing algorithm.
It is made as an improment to study [35] and also, the problem arising from
the iterative nature of the filtering preprocess in the existing algorithm is
solved by designing a filter that is processed only one time so that a fast
recognition speed and reduced hardware complexity is obtained.
For further enhancement noise removal is introduced in A biometric
identification system by extracting hand VPs [52], uses an new filter which
shift and add the binary values to perform noise removal which can be
performed at any point of the image obtained. The method also uses the
FPGA (field programmable gate array) device to speedup the recognition
process.
Illumination invariant face recognition using near-infrared image
[54], present a novel solution for illumination invariant face recognition for
indoor, cooperative-user applications.
First, an active near infrared (NIR) imaging system that is able to
produce face images of good condition regardless of visible lights in the
environment is presented. Second, the resulting face images encode
43
intrinsic information of the face, subject only to a monotonic transform in
the gray tone is showed; based on this, local binary pattern (LBP) features
to compensate for the monotonic transform, thus deriving an illumination
invariant face representation is used. Then, methods for face recognition
using NIR images, statistical learning algorithms are used to extract
most discriminative features from a large pool of invariant LBP features and
construct a highly accurate face matching engine is presented.
Finally, a system that is able to achieve accurate and fast face
recognition in practice, in which a method is provided to deal with specular
reflections of active NIR lights on eyeglasses, a critical issue in active NIR
image-based face recognition is introduced.
Extensive, comparative results are provided to evaluate the imaging
hardware, the face and eye detection algorithms, and the face recognition
algorithms and systems, with respect to various factors, including
illumination, eyeglasses, time lapse, and ethnic groups.
Biometric authentication by hand VPs [55], discuss as the hand VPs
are unique and universal. VP is used as biometric feature in recent years.
But, it is not very much popular biometric system as compared to other
systems like finger print, iris etc, because of the higher cost. For
conventional algorithm, it is necessary to use high quality images, which
44
demand high-priced collection devices. There are two approaches for vein
authentication, these are hand dorsa and hand ventral. Currently the work
going on hand dorsa VPs.
Here the new approach for low cost hand dorsa VP acquisition using
low cost device and proposing a algorithm to extract features from these
low quality images is introduced.
Knuckle profile identity verification system [56], proposes a system
(method and apparatus) for verifying/identifying a person based on contour
of the knuckle surface of at least one hand, e.g. a digitized waveform unique
to each individual.
At an Enrollment Station a microcomputer is connected to a device
for identifying the knuckle surface profile (e.g. a video camera or
electromechanical contour sensing device). A candidate user grasps a grip
handle, preferably vertically oriented, on the apparatus, positioning a fist
before a viewing window and activating the device to scan or assess the fist
and generate a contour of the user's knuckle surface contour. User's data
comprises a knuckle contour, an assigned PIN, and optionally, information
such as user's name, bank ID number, Social Security Number, and access
restrictions.
45
User's data may be stored as a profile (template) in an ID card, and/or
in a master database containing data of all authorized users in a guarded
system.
Hand vein recognition based on multi supplemental features of multi-
classifier fusion decision [61], proposes a new algorithm based on multi
supplemental features of multi-classifier fusion decision is proposed. It
overcomes the disadvantages of the single feature recognition. Experimental
results indicate that the proposed methods can significantly improve the
recognition accuracy and reliability compared to the previous hand vein
recognition.
Near- and Far-Infrared imaging for VP biometric [62], compares two
different imaging techniques like near infrared and far one to the purpose of
extracting palm vein patterns. The paper also performs deep analysis on
these approaches using varying number of samples.
Minutiae Feature Analysis for Infrared Hand VP Biometrics [69] and
clustering the Dorsal Hand VPs using the Firefly Algorithm [98], proposes
a novel technique to analyze the infrared VPs in the back of the hand for
biometric purposes. The technique utilizes the minutiae features extracted
from the VPs for recognition, which include bifurcation points and ending
points.First, the images of blood vessels on back of the hands of people is
46
analyzed, and after pre-processing of images and feature extraction (in the
intersection between the vessels) to identify people firefly clustering
algorithms is used.
This identification is done based on the distance patterns between
crossing vessels and their matching place. The identification will be done
based on the classification of each part of NCUT data set and it consisting
of 2040 dorsal hand vein images.
High speed in patterns recognition and less computation are the
advantages of this method. Similar to finger prints, these feature points are
used as a geometric representation of the shape of VPs. Analysis of a
database of infrared VPs shows a trend that for each hand VP image, there
are, on average, 13 minutiae points in each VP image, including 7
bifurcation and 6 ending points. The modified Hausdorff distance algorithm
is proposed to evaluate the discriminating power of these minutiae for
person verification purposes.
Experimental results show the algorithm reaches 0% of equal
error rate (EER) on the database of 47 distinct subjects, which indicates the
minutiae features of the VP can be used to perform personal verification
tasks. The paper also presents the preprocessing techniques to obtain the
minutiae points as well as in-depth study on their tolerance to processing
47
errors, such as loss of features and geometrical displacement.
Physiology based face recognition in the thermal infrared spectrum
[70], present a novel framework for face recognition based on physiological
information. The motivation behind this effort is to capitalize on the
permanency of innate characteristics that are under the skin.
To establish feasibility, a specific methodology to capture facial
physiological patterns using the bio heat information contained in thermal
imagery is proposed. First, the algorithm delineates the human face from the
background using the Bayesian framework. Then, it localizes the superficial
blood vessel network using image morphology. The extracted vascular
network produces contour shapes that are characteristic to each
individual.
The branching points of the skeletonized vascular network are
referred to as thermal minutia points (TMPs) and constitute the feature
database. To render the method robust to facial pose variations, for each
subject to be stored in the database five different pose images are collected.
During the classification stage, the algorithm first estimates the pose of
the test image. Then, it matches the local and global TMP structures
extracted from the test image with those of the corresponding pose images
in the database.
48
Optical imaging and parametric characterization of frostbite changes
in human hand tissues [74], deals with the optical characterization of
frostbite of the hands, which involves the skin and tissue structure beneath
it and is carried out by laser reflectometry and Monte Carlo simulation. The
grid of the dorsal side of the hand is developed on a computer monitor and
the same is scanned point-to-point.
Data obtained in the form of normalized back-scattered intensity
(NBI), after interpolation and median filtering are displayed as color-coded
images. In controls the NBI is significantly higher at the abductor brevis
muscle and minimum at the tendon of the flexor digitorum, pollicislongus
and at the nails compared to that at the other regions.
Due to frostbite the NBI values are lower at various locations in the
fingers and dorsal sites in these subjects compared to those of the respective
controls. The variation in NBI is maximum for the first probe compared to
the other probes.
A Novel Biometric system for Person Recognition Using PV Images
[85], conducts a comprehensive comparative study of three local invariant
feature extraction algorithms: Scale Invariant Feature Transform (SIFT),
Speeded-Up Robust Features (SURF) and Affine-SIFT (ASIFT) for PV
recognition. First, the images were preprocessed through histogram
49
equalization, then three algorithms were used to extract local features, and
finally the results were obtained by comparing the Euclidean distance.
Experiments show that they achieve good performances on our own
database and multispectral palm print database.
In Enhanced PV Recognition System Using Multi-level Fusion of
Multimodal Features and Adaptive Resonance Theory [88], uses shape and
texture features have been extracted and multimodal features have been
obtained at feature extraction level as well as matching score level.
The Neural network classifier has been used to classify the VPs for
making necessary decision.
It is concluded from the analysis that the multimodal PV recognition
system provides better performance compared uni modal features.
PV recognition using curve let transform [87], technique utilizes the
curve let transform to extract curve-like features from VPs and provides
optimally sparse representations of the patterns. For evaluation, HK Poly
University multispectral palm print database is used.
Contactless PV identification using multiple representations [2],
investigates some promising approaches for the automated personal
identification using contactless PV imaging (from which sudy [95] made as
an advancement proposed paper). First they present two new PV
50
representations, using Hessian phase information from the
enhanced vascular patterns in the normalized images and secondly
from the orientation encoding of PV line-like patterns using
localized Radon transform.
The comparison and combination of these two PV feature
representations, along with others in the PV literature, is presented for the
contactless PV identification. In addition to the contactless approach of PV
identification, Contactless PV Identification using Multiple Representations
[95], investigates some promising approaches for the automated personal
identification using contactless PV imaging. The method uses different
representation of palm vein features one in form of hessian matrix and
another in form of line like patterns.
The comparison and combination of these two PV feature
representations, along with others in the PV literature, is presented for the
contactless PV identification. Also evaluate the performance from various
PV representations when the numbers of training samples are varied from
minimum.
The recognition rate of this method is more accurate and the error is
less than one percent. At the end the correctness percentage of this method
for identification is compared with other various algorithms, and the
51
superiority of the proposed method is proved.
Problem Statement Biometrics presented techniques for
authenticating group of people by using bodily personality features. A
variety of biometric identification methods such as PV, facial recognition,
finger print, iris etc,
1. These conservative schemes contain some difficulties in conditions
of feasibility and arrangement in palm print and finger print identification
schemes, customers encompass to handle the outer of the contribution
antenna by their palm and finger.
2. This could be based on a large amount of problems for the
customer and it was too probable to take dormant in sequence from the PV
antenna.
3. In adding together, the state of the palm outside and coat bend can
cause humiliated identification precision.
4. In facial identification, presentations extremely depended on
face appearances and enlightenments which can modify.
5. Iris identification mainly depends on conditions of precision, but
the detaining tool is luxurious and can be difficult contrasted to further
biometric schemes.
52
Problem Solution
To overcome the identified problems:
1. Vein prototypes such as PVs and hand veins had been considered.
2. PV identification used interior sequence as of a human being‘s
remains and vein prototypes which could be seen with infrared beam
illuminators and a capturing device. As well as, it was tough to get interior
models.
Summary
Biometrics is a method by which a person's authentication
information is generated by digitizing measurements of a physiological or
behavioral characteristic. Biometric authentication checks user's claimed
identity by comparing an encoded value with a stored value of the
concerned biometric characteristic. Various biometric authentications are
face recognition, finger prints, hand geometry etc. Among this, the most
recent technology is PV authentication. Various techniques have been
proposed by researchers in the area of PV identification. Most of the
methods uses various features of PV like geometric, cosine similarity,
wavelet features etc but lags with the accuracy of identification and
authentication. Authentication using hand geometry does not have the same
degree of permanence or individuality as other characteristics. Even
53
authentication using Cosine similarity and wavelet features lags in
accuracy. PV authentication is highly accurate and secure since the
authentication data exists inside the body and it is difficult to forge. It uses
vascular patterns as personal identification data. This paper presents the
analysis of various methods and algorithms that identifies the VPs in palm
for authentication purpose.
Table 2.1 Analysis of Various Techniques
Methods Results Limitation
1.Using multiscale
wavelet edge detection
Efficiency is 100%. Sensitive to noise,
discriminating between
edges
2.Using adaptive
Gabor filter
EER is 0.6%,High
accuracy and speed
Better using directional
filter bank method
3.Using Hybrid
Principal Component
EER was 9.839% for
unscaled PCA-ANN
EER has lower pixels
resolution (46.37) for
scaled PCA-ANN
Literature presents the various techniques for PV recognition. In this
conventional edge detection techniques suffer from limitation like
sensitivity to noise, discriminating between edges etc. the table clearly
shows that PV recognition using neural network is quite efficient and
accurate.
54
CHAPTER 3
PV RECOGNITION SYSTEM USING LOCAL BINARY PATTERN
AND GABOR FILTER USING CLAHE BASED CONTRAST
ENHANCEMENT METHOD
3.1 Introduction
Biometrics refers to methods for recognizing people by using
physical human features. There have been several kinds of biometric
recognition systems such as PV identification, face identification, iris
identification, finger print identification etc. However, these conservative
systems have some difficulties in conditions of expediency and
presentation.
In PV and finger print recognition systems, users have to touch the
outside of the input sensor by their palm and finger. This can cause a large
amount of problems for the user and it is also probable to take dormant
information from the PV sensor.
In consideration, the state of the palm surface (e.g. sweat, dryness)
and skin bend can cause humiliated identification accuracy. For face
recognition, presentation extremely depends on facial expressions and
enlightenments, which can be altered. Iris identification is most
55
dependable in conditions of accuracy, but the capturing tool is luxurious and
can be inconvenient contrasted to further biometric systems.
To rise above these problems, VPs such as PVs [13] and hand veins
have been studied [14]. PV recognition uses interior information as of a
person‘s body and VPs, which can be seen with IR (Infrared)
illuminators and a camera. Moreover, it is hard to pick up internal patterns.
However, the finger print and hand vein recognition tool is presently too
large compared to PV recognition systems.
3.2 Previous Research
Yanagawa‘s study established that PV prototypes could be
appropriately used for individual classification [1]. They demonstrated that
the palm of every human being has completely dissimilar VPs and a finger
vein outline shows as much degrees of freedom as iris patterns [1]. Miura
proposed a method of removing palm VPs by using frequent line tracking
from a variety of initial places. There proved to be high-quality extraction of
presentation with look upon to image shading [2]. These researchers used
678 PV images for identification, while the accuracy was 85% and
the processing time was 10mille seconds [2]. Zhang also proposed a
removal technique based on the curvelet information of the shape of PV
56
images and nearby unified planned neural networks [3]. The proposed
neural networks were qualified using information from true and false
VP (Vein Pattern) regions [3]. These researchers used exactly 3200 PV
images for matching while the accuracy was 88% [3]. In the majority of
current study, Miura showed that the thickness of palm VPs could
change in palms under dissimilar weather situations (shown by using 678
PV images with an accuracy of 90%) [4]. Miura proposed palm VP
extraction based on the curvature value on a cross part of a palm VP and
extracted the points with high curvature values in each of four instructions.
This allowed to extracting a palm VP even in the middle of a variety
of pattern thicknesses [4]. Profitable manufactured goods have also been
introduced by Hitachi [15]. In all these PV removal method is used.
However, PV images are not always clear, but can sometimes also show
irregular shadings and highly saturated areas. Therefore, detection errors can
arise when extracting exact vein prototypes. Also, the PV extraction step can
lead to increase of processing time. To overcome these troubles, a new
PV identification system is offered. In this identification system a Local
Binary Pattern method is used for removing local information of PV
outlines. Using thLBP, identification presentation was more consistent
adjacent to irregular shadings and highly flooded sections. In addition, the
57
overall information of PV outlines based on Wavelet transform is removed.
The two attained standards by the LBP and Wavelet transform were
joined by the SVM (Support Vector Machine) and the authentication
performance was much superior.
3.3 Block Diagram
Figure 3.1 Block Diagram of Gabor Filter and Local Binary Pattern
3.4 Methodologies
CLAHE based Contrast Enhancement
Fast Discrete Curvelet Transform
58
Texture Characterization:
Local Binary Pattern – Gabor Filter
Matching: Euclidean Distance
Performance metrics: Sensitivity(recall), quality of being specific,
precision and accuracy
3.5 Input, Selected Region and Results of Combined Features
Figure 3.2 Examples of Input PV Region
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The figure 3.2 shows the snapshot of selected input image to perform
authentication. A right hand with inclined image which has to be rotated
to get the features of the palm VP is selected.
Figure 3.3 Examples of Selected PV Region
The figure 3.3 shows the snapshot of selected PV region being
extracted from the input image and it shows clearly that the portion
marked in the previous stage has been extracted for further processing.
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Figure 3.4 VP
The figure 3.4 shows the snapshot of image which is removed from
noise present in the ROI image. It shows that the image has been taken out
from the noise present in the image which shows the Region of interest
image after performing the background subtraction process and
the background subtraction has been performed by binary imaging
technique.
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The figure 3.4 shows the snapshot of normalized palm VP which
will be used to perform skeleton identification process. The normalized
image has veins with bigger dimensions so that the veins skeleton has
to be identified to perform junction point identification and to perform other
computation process.
Figure 3.5 Curvelet Decomposition Pattern
The figure 3.5 shows the details of Curvelet Decomposition Pattern
points which have been identified from the given input image. Such points
will be used to compute the volumetric measure between the points.
62
Figure 3.6 Gabor Texture Representation Regions
The figure 3.6 shows the snapshot of Gabor Texture Representation
result produced by methodology. It shows clearly that the proposed method
has produced efficient results.
The above discussions have been in presented; the proposed method
with various stages of results. The method has figured out different junction
points from the input image and has computed volumetric measure
between all the junction points. By calculating the volumetric measure
63
between all the measures a cumulative special volume has been computed.
This special volume has been used to compute trustworthy measure of the
given input image to perform authentication of the PV image.
Figure 3.7 LBP Texture Representations
The figure 3.7 shows the snapshot of LBP Texture Representation
result produced by methodology. It shows clearly that the proposed method
has produced efficient results. The method reads the input image and applies
Gabor filter which removes the noise from the image. The noise removed
image is applied with histogram equalization technique which enhances the
64
input image quality. The quality improved image is applied with wavelet
analysis which splits higher and lower signals to produce two
different images where the dorsal VP is in the higher order image and the
lower order image represents the deep VPs. The two images are split
into number of small images and their features are extracted to identify the
junction points. The extracted junction points are stored in a dorsal junction
matrix and deep junction matrix. Based on the two matrixes generated the
dorsal vein depth measure and deep vein depth measures are computed.
Using these two measures a cumulative depth is computed on which the
person identification is performed.
65
Figure 3.8 LBP and Gabor Performance
The figure 3.8 shows the comparison of PV authentication accuracy
produced by different methods. The proposed methods have produced
efficient accuracy than the other methods at different number of classes and
samples. The proposed Texture Characterization Method has 2 approaches
Namely Local Binary Pattern and Gabor Filter. The method removes the
66
noise and performs histogram equalization to enhance the image. The
enhanced image is applied with wavelet analysis and splits the higher order
and lower order VP. Generated two different images are split into sub
sample images and their junction points are identified. Identified junction
point matrix is used to compute the dorsal depth and deep vein depth
measure to compute the cumulative weight. Based on cumulative weight an
average distance measure is computed to identify the person base on some
threshold value. The proposed method has produced efficient results
compared to other methods.
67
Figure 3.9 Individual Samples of Recognition
The figure 3.9 shows a PCA performance graph between number of
samples/subjects and recognition percentage. Recognition percentage
reaches half the level during number of samples are equal to 9.
68
Figure 3.10 Samples Vary with Recognition Percentage – Set 1
The figure 3.10 shows a gabor performance graph between number of
samples/subjects and recognition percentage. Recognition percentage
reaches 60% of the level during number of samples are equal to 9.
69
Figure 3.11 Samples Vary with Recognition Percentage – Set 2
The figure 3.11 shows a LBP performance graph between number of
samples/subjects and recognition percentage. Recognition percentage
reaches 60% of the level during number of samples are equal to 8.
70
Figure 3.12 Comparisons of PCA and Gabor
The figure 3.12 shows a PCA (Vs) gabor performance graph between
number of samples/subjects and recognition percentage. Recognition
percentage of PCA reaches 50% of the level during number of samples are
equal to 9. Recognition percentage of gabor reaches 60% of the level
during number of samples are equal to 9.
71
Figure 3.13 Comparisons of PCA and LBP
The figure 3.13 shows a PCA (Vs) LBP performance graph between
number of samples/subjects and recognition percentage. Recognition
percentage of LBP reaches 60% of the level during number of samples are
equal to 8. Recognition percentage of PCA reaches 40% of the level
during number of samples are equal to 8.
72
Figure 3.14 Comparisons of PCA and Gabor, LBP
The figure 3.14 shows a PCA (Vs) proposed system (gabor+LBP)
performance graph between number of samples/subjects and recognition
percentage. Recognition percentage of PCA reaches 40% of the level during
number of samples are equal to 8. Recognition percentage of proposed
system reaches 70% of the level during number of samples are
73
equal to 8.
Figure 3.15 Comparisons of PCA and Gabor, LBP Version with
Number of Samples
The figure 3.15 shows a LBP (Vs) proposed system (gabor+LBP)
performance graph between number of samples/subjects and recognition
percentage. Recognition percentage of LBP reaches 60% of the level during
74
number of samples are equal to 8. Recognition percentage of proposed
system reaches 70% of the level during number of samples are
equal to 8.
Figure 3.16 Comparison of Other Methods with the Proposed
75
The figure 3.16 shows LBP (Vs) proposed system (gabor+LBP) (Vs)
gabor (Vs) PCA performance graph between number of samples/subjects
and recognition percentage. Recognition percentage of LBP reaches 60% of
the level during number of samples are equal to 8. Recognition percentage
of proposed system reaches 70% of the level during number of samples
are equal to 8. Recognition percentage of PCA reaches 40% of the level
during number of samples are equal to 8. Recognition percentage of gabor
reaches 55% of the level during number of samples are equal to 8.
3.6 Architecture of PV Recognition
The PV images are got from training images and testing images. The
regions of PV images are localized and further process followed in about
stretching and sub-sampling. The PV images are now ready for the
extraction and extraction is carried out in two parallel ways.
1. Extracting PV code using LBP
2. Extracting PV feature values by using wavelet
The calculated humming and Euclidean distance from the above two
extraction process are proceded with SVM classification where 2000 PV
images from database are present. After the resultant verification, the result
may be matching or non-matching.
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Algorithm steps
Step1: Capturing PV Image from Device
Step2: Localizing the PV Region by using masks
Step3: Stretching and sub-sampling the localized Region
Step4: Extracting the PV code by using LBP (Local Features)
if x≥0, Initialize n=0
if x<0, s (in-ic)^2^n
Step5: Matching the extracted codes with enrolled ones
Step6: Extracting the Wavelet Transformed Features (Global
Features)
Step7: SVM classification by using Hamming Distance and
Euclidean Distance
d‘=μ1-μ2/√σ1^2 +σ2^2/2
step8: Result verification based on the two distance values, like matching
or Non-matching.
77
Figure 3.17 Architecture of PV Recognition
78
3.7 Junction Point (JP) Detection
The junction points are nothing but the connection of two edges. In
PV image there will be number of lines according to the VP of human.
There will be number of edges meet and cross which generates junction
points. Such junctions are generated through the cross and meet of different
veins of human vessels which passes bloods. The biometric authentication
has greater impact of such junction points and in this case the junction points
are more efficient and unique than other features of human biology. By
identifying the junction points of human PV image, the number of points and
edges can be identified and they are unique for each person which can be
used to perform biometric authentication.
The crossings point of the central B is given as below
B=∑^8 n=1 |s (Qn+1)-s (Qn)|, s (Q9) =s (Q1) (1)
Where s (x) is the binary value (0or1)of pixel at x. The centre
point Q is considered to be a junction point if Q is an edge point and if
B>=6
3.8 Cross Correlation on Join Point Extraction
The connection of two joints can be extracted by performing cross
correlation and can be performed using the below equation.
79
Sab=A^i U B^i U/√1|A^i u||Ci||B^i U| (2)
In this equation the value of variable A is predefined and B is the
result obtained. Also the value of Sab is 1 when the authentication is
successful.
3.9 Limiting the PV Region
The PV images are integrated with the shaded sections at both ends in
the flat way. Because of this, the image is corrupted. So shared portions can
only be cut off with fixed pixel sizes at both ends. The outline of each
person‘s palms is dissimilar. So the PV section in order to standardize the
PV figure and take out the surface from the standardized image was
identified. The PV region is more dazzling than the setting area, because
infrared radiance shining throughout the skin. Therefore, in order to contain
the PV portion from captured images, the covering value was calculated
in the Y path for each X point and the place at which the covering
value became maximal was resolute as the border arrangement among
the palm and the setting in the Y path. Figure 3.18 shows the result of
localizing the palm region with masks.
80
Figure 3.18 Examples of Localizing the PV Region with Masks
3.10 Extending and Sub-Sampling the contained Region
The contained PV region in the way of the X and Y axis is
extended. As a result, a 150 × 60 pixel extended image was formed. Then,
in order to develop the dispensation time, the extended figure was converted
to 50 × 20 pixels by averaging the hoary values for every 3 × 3 pixel block.
By using sub-sampled descriptions, the removed PV skin textures became
tough not in favor of noise aspects. The 3 × 3 pixel block was resolute
based on the breadth of the thinnest vein in the extended figure (which was
calculated as 3 points by experimentation).
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Figure 3.19 Stretched Images of Figure
3.11 Extracting the Palm Vein Code by Using the Local Binary
Pattern
In preceding PV authentication, the technique took a large amount of
dispensation time because it was essential to identify PV areas before
removing the skin textures. So, the technique suffered that authentication
presentation was affected by PV recognition faults. To overcome this
trouble, a Local Binary Pattern technique that could take out the PV
codes in the whole vein area exclusive of requiring exact recognition of that
area is used. Because the LBP technique compares limited sections, it is
robust against high dispersion and asymmetrical shadings in the captured
82
image. The value of the LBP established the LBP operative tool which was
a non-parametric 3 × 3 kernel for quality categorization. The LBP can be
defined as a prearranged set of binary standards resolute by evaluating
the hoary standards of a middle point and the 8 neighborhood region pixels
around the center. The prearranged set of binary values can be uttered in
decimal form as shown by Equation (1).
LBP (xc, yc) = ∑^7 n=0 s (in-ic)^2^n (1)
Where ic and in represent the hoary assessment of the middle point
(xc, yc) and the values of hoary for the 8 adjoining pixels correspondingly.
The function s(x) is defined as follows.
S(x )= 1, if x≥0
0, if x<0 (2)
Figure 3.20 The LBP Operator
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The figure 3.20 shows about the binary intensity of the LBP Operator.
The binary value of 0111000 is equated with the decimal value 120.
3.12 Matching the Extracted Codes with Enrolled Ones
The proposed PV recognition compute the HD value which is
equated to the extracted codes with the registered ones. The HD is used to
calculate the distinction between any two PVs, as r epresented in
Equation (3):
HD=||(code A * code B) U mask A U mask
B|| / mask A U mask B || (3)
The code A and code B values are the take out from the PV code
vector and the registered one, correspondingly. The mask A and mask B
values are extracted from the control code vector and from the registered
one, respectively.
Authentication presentation is decreased by evaluating the skin
sections of the captured PV image with the skin regions of registered ones.
Therefore, a direct policy to decide whether the extracted PV code was
accessible or not. (If the code extracting location was a vein region, the
control code was 1, whereas, if the code extraction position was a skin
region, the control code was 0 and it was not used for identification.) To
84
decide the types of direct codes of the code extraction position, the standard
deviation of the 3 × 3 block in which LBP extracted code was calculated.
If a vein was included in the 3 × 3 block, the standard deviation was high
because the difference between the gray values of the vein and the skin was
high. The threshold of the standard deviation with the accuracy of
maximized PV recognition is calculated.
3.13 Extracting Wavelet Transformed Features: Global Features
The PV code by LBP represents the local and detail features in 3 ×3
block. To enhance the recognition accuracy, global features by using
Wavelet transform was extracted. There exist many kinds of Wavelet basis
such as Haar, Gabor, Daubechies. Experimental results showed that the
accuracy of PV recognition was smallest in case of using Gabor basis for
Wavelet transform. Optimal frequency and kernel size of Gabor bases were
selected based on the minimum authentication of accuracy with the training
data of 2 000 images. First, multi-resolution decomposition of stretched
and sub-sampled PV region are carried out. From that, four sub regions
were defined: LL (low-frequency component in both the horizontal and
vertical directions), HL (high and low-frequency component in the
horizontal and vertical directions, respectively), LH (low and high-
85
frequency component in the horizontal and vertical directions, respectively),
and HH (high-frequency component in both the horizontal and vertical
directions).
Then, each sub region was decomposed again. From that, 16 sub
regions (two-level decomposition) are obtained. Then, 16 sub regions were
decomposed again and 64 sub regions (three-level decomposition) are
obtained. From the 64 sub regions, the mean and standard deviations of sub
region are measured and obtained as 128 (= 64 sub regions × 2 features)
features. The number of decomposition level was determined with which
maximum accuracy of PV recognition was obtained. Greater weight
values to the extracted features from LH region than those from other
regions. The optimal weight values were selected based on the maximum
authentication of accuracy with the training data of 2 000 images.
Then, the Euclidean distance between the extracted 128 feature values
and the enrolled one was calculated. Because the features have continuous
value, Hamming distance could not be used. By comparing the accuracy
of PV recognition in case of using Euclidean distance and cosine distance,
the Euclidean distance which showed high accuracy rate was
calculated. Whereas the PV code of 6,912 bits by LBP represented local
86
characteristics of PV such as detail shape of PV region, 128 feature values
by Wavelet transform did global characteristics such as the rough shape of
PV region.
3.14 SVM Classification
To combine the Hamming distance (HD) by LBP and the Euclidean
distance (ED) by Wavelet transform, a SVM (Support Vector Machine) was
used. In the past, the SVM was used to solve two class problems by
determining the optimal decision hyper plane. It is based on the concept of
structural risk minimization, since it measures the maximum distance to the
closest points of the training set. These measurements are known as support
vectors. For SVM training, half the images in the dataset were used. The
other half was used for testing. Two distances (HD and ED) were used as
the input values of the SVM. The output value of the SVM was
represented as a continuous value. A value that was close to 1
represented a genuine user and a value that was close to −1 represented an
impostor. The genuine means the user whose PV code and feature were
enrolled legally. The impostor is the user whose vein code and features
were not enrolled. From recognition performance based on the accuracy
and the prime was the output values of SVM, a genuine or impostor user
87
based on threshold was determined. The threshold limit was fixed with the
training data of 2000 images. Because the outputs of SVM is categorized
into two distributions such as genuine and imposter ones, the optimal
threshold based on conventional Bayesian rule was determined.
3.15 Experimental Results
In the first experiment, the recognition performance was tested.
First, measured. The d prime value means the classifying ability
between authentic and impostor distribution, defined by Equation (4)
d‘=μ1-μ2/√σ1^2 +σ2^2/2 (4)
Where μ1 and μ2 represent the means of authentic and imposter
distributions, respectively, and σ1 and σ2 represent the standard
deviations of authentic and imposter distributions. The greater the d′
value was, the more separable the two distributions and the accuracy
became higher. The accuracy and d prime of recognition are shown in
Table 3.1.
In case of ―only using HD by LBP (Local Feature)‖ and ―only
using ED by Wavelet transform (Global Feature).‖ The proposed method
requires half of data for the training of SVM, total 2000 images were used
for training and the other 2000 images were used for testing.
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Although the shifting and matching scheme of PV code and features
were used, to pre-align the PV image based on the detected PV region and
minutia points such as bifurcation and ending points of PV lines is planned.
3.16 Summary
Table 3.1 The Accuracy Rate of PV Images
Methods No. of
PV
Accuracy
(%)
Processing
Time (mille
PV recognition with correlation
method using curvelet and Neural
2000
82%
13ms
PV recognition with correlation
method using line tracking algorithm
2000
85%
11ms
PV recognition with Correlation
Method only
2000
90%
9ms
Our Proposed Method (Correlation
with SVM classification)
2000
99%
6ms
A novel PV recognition algorithm was proposed. The proposed
algorithm is robust against irregular shading and saturation factors by using
the local and global features. As a result, the accuracy was 99% and the entire
processing time was 6 mins.
89
Figure 3.21 Graphical Representation of Accuracy
Performance of PV Images
The figure 3.21 shows graph for correlation method only recognition,
with line tracking algorithm and SVM based recognition. SVM based
recognition had reached more number of tested PV images for 2000 trained
PV images compared to correlated method. In future work, pre-aligning the
PV image based on the detected PV region and minutia points such as
bifurcation and ending points of PV lines is planned. To increase the
90
dataset including more different ages, genders and occupations is also
aimed.
Figure 3.22 Graphical Representation of Processing Time
Performance of PV Images
The figure 3.22 shows graph for curveletand neural networks based
recognition, line tracking algorithm and SVM based recognition.
Curveletand neural networks based recognition had reached more number
of processing time performance in mille seconds for 500 PV images
compared to line tracking algorithm and SVM based recognition.
91
CHAPTER 4
MULTI-VARIANT VOLUMETRIC MEASURE ON UPPER
EXTREMITY VP BASED PV RECOGNITION USING WAVELET
TRANSFORM
4.1 Introduction
The PV recognition has become most popular person authentication
approach where the recognition part is more complicated and has many
stages. Generally the human hand VP is used for authentication purpose
which is more secure and could not be malformed by others. The nature of
VPs and structural information supports the authentication process to be
performed in efficient manner.
The palm VP of any person is captured using the scanners of
ultrasonic type which is taken by passing the waves to the part of the hand.
The human vessels and veins are hard enough to reflect the ray comes from
the scanner. The reflected rays are captured to produce the PV and the
image acquired by this process becomes the only source to perform
authentication process.
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Figure 4.1 Palm VP of Hand
From the figure 4.1 the deep veins and superficial veins interlinked
each other frequently. The deep veins have arteries and have vanaeco
mitantes of the veins. The superficial veins have the following types
namely,
Digital Veins - The dorsal digital veins located and pass through
the sides of fingers and they are joined each other through
communicating branches. This makes three metacarpal veins and extends up
to the dorsal venus network.
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Cephalic Vein – it starts at the radical side of the venus network
and grows upward towards the radical part of the forearm.
Basilic Vein – starts at the venus network and grows through the
surface of forearm and inclined through the anterior surface.
Dorsal Metacarpal veins- The dorsal digital veins which surround the
fingers joins to form a three vein junction called metacarpal veins. Median
Vein- starts from the venous plexus and ends at the basilica vein.
Figure 4.2 Abstract VP of Human Hand
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The figure 4.2 shows the abstract view of palm VP and shows
the arteries, nerves and veins present in the palm VP of human.
From the above discussion, even though all the human has VP
w i t h the temporal features like shape, size vary between users and it will
be a distinct one for each other. In this approach such features to develop an
authentication framework to perform person authentication using PV images
are considered.
4.2 Methods Explored
Various approaches for personal authentication using PV images are
explored. PV Recognition Based on Three Local Invariant Feature
Extraction Algorithms Biometric Recognition [1], In contrast to minutiae
features; local invariant features extracted from infrared PV have scale
property, translationproperty and rotation invariance property. To
determine how they can be best used for PV system of recognition,
comparative study is made on this paper based on comprehensive of three
local invariant feature extraction algorithms: SIFT - Scale Invariant Feature
Transform, SURF - Speeded-Up Robust Features and ASIFT – Affine SIFT
for PV recognition. Initially, the preprocessing of image is done through
histogram equalization, then local features are extracted by three algorithms
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and at last the result was obtained by comparing the distance of Euclidean.
Good performances on our own database and Poly multispectral palm print
database is obtained with the experiments.
PV Verification System Based on SIFT Matching [2], present in this
communication a new biometric system based on the use of hand veins
acquired by an infrared imager. The vein image is characterized by
particular patterns after the binarization and preprocessing stage. One of the
unique work in the proposed system is to use SIFT descriptors for the
process of verification. The developed method only makes it necessary for a
single image for the enrollment step allowing a very fast verification. The
results obtained after the experiment on a database containing images of 24
individuals acquired after two sessions show the efficiency of the proposed
method.
PV recognition by combining curvelet Transform and Gabor filter [3],
the Curvelet Transform is good at extracting the linear features from the
images of PV and the excels in extraction of orientation features by Gabor
Filter. On the basis of investigating the above two different coding schemes,
in this paper a score-level fusion scheme for palm print/vein verification is
proposed. The proposed method was applied on the HK PolyU Database and
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an EER of 0.1023% was achieved, which outperforms using the Curvelet
Transform or Gabor Filter alone.
PV recognition by combining curvelet Transform and Gabor filter [4],
Biometrics research based on PV recognition has been developed rapidly in
recent years. PV extraction and matching for personal authentication [5],
propose a scheme of PV based personal authentication. The method captures
the infrared PV image through the capturing device then applies multilevel
filter. Then it extract the features and performs matching to improve the
quality of biometric authentication.
In PV recognition with Local Binary Patterns and Local
Derivative Patterns [6], a promising new approach based on local texture
patterns is proposed. In initial stage, histograms of multi-scale Local Binary
Patterns (LBPs) and operators are investigated in order to identify new
efficient descriptors for palm VPs. Higher-order novel local pattern
descriptor based on Local Derivative Pattern - LDP histograms are then
investigated for PV description. The extraction methods are compared with
both features and evaluated in the framework of verification and
identification tasks. Extensive experiments on CASIA database - CASIA
Multi-Spectral Palm print Image Database V1.0 finds the LDP and LBP
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descriptors which are better adapted to PV texture. Tests results on the
CASIA datasets shows that the best adapted LDP descriptors consistently
outperform their LBP counterparts in both PV verification and identification.
Palm-Vein Image Recognition of Human Using Discrete
Enhancement [9], the palm-vein-based approach attempts to be
more effectively accommodating the potential deformations, revolving and
translational changes by encoding the orientation preserving features. The
method takes the infrared PV image as the input and identifies the junction
point using the hand geometry algorithm and pose invariant algorithm to
handle the shape and position of the image. Finally the method computes the
rank matrix using which biometric authentication is performed.
PV Recognition System Using Hybrid Principal Component
Analysis and Artificial Neural Network [10], focuses on PV recognition
system using Hybrid Principal Component Analysis (PCA) and Self
Organizing Map (SOM). The PCA-ANN experiments were considered
twice when inputs to ANN were unscaled (raw scale between 0 and 255)
and scaled (scale between 0 and 0.9). The systems performance was
evaluated on the basis of different image resolutions, different
training datasets, and recognition time and accuracy of recognition. The
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scaled PCA-ANN and unscaled PCA-ANN gave an optimal recognition
accuracies of between 55% and 98% and 56%-99% respectively at a
resolution of between 30*30 and 60*60 pixels level of cropping. Also
perform the further experiments in determining the error rates so that the
scalability of the algorithms to the task of controlling access will be
investigated. The FAR and FRR were between 2.5%-12.5% for unscaled and
2.5-15% for scaled and 2%-82% for Unscaled and 1%-81% for scaled at
0.0001 threshold accordingly. EER was in the level of 9.839% for unscaled
PCA-ANN at 49.53 range of pixels resolution and 12.53% for the scaled
PCA-ANN at 46.37 range of pixels resolution. This showed that EER was
achieved at lower pixels resolution (46.37) for scaled PCA-ANN than
the unscaled PCA-ANN (49.53) which revealed that overall system
accuracy would optimally be attained by scaled PCA-ANN than the unscaled
PCA-ANN.
All the above discussed approaches have the problem of false
acceptance rate and higher error rate conditions. To overcome the problem
of false acceptance rate, a multi variant volumetric measure based PV
authentication approach is proposed.
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4.3 Overview of Multi-Variant volumetric Approach
In this approach (Figure 4.3) the user has to keep his hand over the
sensor for certain time and the scanner captures the VP using ultrasonic
rays. The captured image is resized to a fixed size and applied with wavelet
transform. The transformed image is applied with edge detection process to
get the concrete VPs. The edge detected image is then split into number of
integral images and for each integral image generated a number of junction
points are identified with their co-ordinates. With the number of interest
points and their co-ordinates, the volumetric measure is computed.
Using all these details, trustworthy of the user for each class is computed
and verified.
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Figure 4.3 Proposed System Architecture-I
The proposed PV recognition approach has various stages and each
of them is discussed here in detail.
4.4 Normalization
The captured input PV image may have various sizes due to
the placement of hands over the sensor. Sometimes the user may keep the
hands with concave manner which reduces the size of the image to be
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vary with the placement of hands in normal without any space between
finger. To overcome this issue normalization is performed (Figure 4.4).
The input image is resized to a fixed size where the training set has the
same. For example if the training set has the image size of 300 pixels
then the input image also will be converted to the same size.
Figure 4.4 Block Diagram of Normalization
4.5 Wavelet Transform on Input Image
The normalized image is applied with wavelet transform to boost the
low level intensity pixels. The input image may contain different contrast
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pixels, and need to be enhanced to identify the VPs. To perform this
activity wavelet transform which is well proven method for signal
processing is used. The wavelet transform increases the signal values
or the pixel intensity which makes the pixels of the image to be visible
and helps the edge detection process to come up with more efficient results.
4.6 Canny Edge Detection
The transformed image is applied with the canny edge detection
process. The canny edge detection is an efficient edge detection approach
which is performed using the gray scale values of the pixels. First the input
image is smoothened using the gradient filter which removes the noise
present in the image. At the next step the method identifies the location or
the pixel where the gradient value deviates in more range. Upon
identifying the location where the gradient changes more, the neighboring
pixels are identified and rounded and preserve the pixel. At the fourth
stage, double Thrseholding is applied and edge tracking is performed to
produce the final edge detected image.
4.7 Integral Image Generation
The integral image is generated using the box type filters which are
the small set of images generated using box filters which splits images
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into many number of sub image set. The input image is selected and
number of sub images is created based on the parameters m and n. Here
m and n specifies the width and height of the integral image to be
generated. The value of m and n is a width and height multiplier of the
image. For example for a image with size 300×300, the value of m and n
will be 3×5 or 5×3 and so on.
4.8 Feature Extraction
The method extracts the gray features from the each integral image
generated and the gray scale values of the pixels which are greater than the
threshold will be identified and such pixel are selected for further
processing. The more gray scale values on gray pixels forms the edges of
the PV and such pixels are identified using which junction points can be
identified. For each integral image the extracted features are represented
in a PV matrix where each pixel selected for processing is represented as
1‘s and others are kept as 0‘s. The generated PV matrix will be used to
identify the junction points and their co-ordinates in the PV image.
4.9 Junction Point Identification
The junction points are identified using the PV matrix where each
index of the matrix with the value 1 is used. The neighbors region is divided
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0
0
0
0
1
1
0
1
0
as 8 quarters and for each 45 degrees there exist a pixel and its value is
available in the PV matrix. From the neighbors and their values, the
pixels with positive notations are identified. For each angle considered, the
presence of positive pixel in the consecutive direction is verified and the
changes of angle in the pixel values are identified. If the pixel is at the
center, and the first top pixel and first bottom pixel are identified.
If the line is a straight one then both the pixels has to be positive
otherwise that are considered as a junction. Further the direction of both
the lines and store the values of coordinates and their count are
identified.
0
1
0
0
1
0
0
1
0
A B
TheTableA,shows that there is no junction or intersection present in
the integral PV matrix.
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0
1
1
0
1
0
0
0
0
0
0
0
0
1
0
0
1
1
The Table B shows that there exist a up-right intersection or
junction present in the PV matrix.
C D
From the table C, there is a right-down junction present in the
PV matrix and in the table D there exist left down junction present.
0
0
0
0
1
0
1
1
0
E F
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0
1
0
0
1
0
1
0
0
0
1
0
0
1
0
0
0
1
The table E shows the right-up intersection present in the matrix
and F shows the down-right intersection from the PV matrix.
1
0
0
0
1
0
0
1
0
G H
The tables G and H show the presence of inclined right
down intersection and inclined down left intersection in the PV.
I J
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1
0
1
0
1
0
0
0
0
0
0
1
0
1
0
0
0
1
The table I and J show the presence of inclined left down and
inclined downright intersection in the PV matrix.
0
0
0
0
1
0
1
0
1
K L
The table K shows the triangular up intersection and the table L shows
the presence of triangular down intersection.
1
0
0
0
1
0
1
0
0
M N
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∫
The table M and N show the presence of triangular intersection from
the PV matrix. The presence of junction points are verified using all these
stage of identification and identified intersection and the co-ordinate are
stored in a separate matrix named junction point co-ordinate matrix.
Algorithm of Junction Point Identification
Step1 : Read integral image IE
Step2 : Convert the image into PV matrix PVM.
Step3 : Initialize angle index AI=0, junction point matrix JPM.
Step4 : Split PVM into number of 3*3 matrix
Step5 : Locate the center of the matrix
For each angle of 45 degrees
If ( , , ( × 45) == 1,1,0) then
AI=n×45
End
End
For each angle of degrees 45
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∫
( (
If ( , , ( × 45) == 1,1,0)&&
( × 45)! = && × 45) − 160)! =
JPM(i) = co-ordinate of Pi.
Junction point count JPC=JPC+1.
End.
Step 6: Stop.
Table 4.1 Displays the Values of Junction Point Matrix
1,17,16
1,29,48
1,72,98
2,15,12
2,34,56
2,87,23
…
…
…
The table 4.1 shows the junction point matrix generated by the
proposed method. Each index has three values where the first value
represents the number of integral image, because there exist N number of
integral image generated from the proposed method. The second index
represents the x- coordinate value and the third value shows the Y-
coordinate position of the junction point. Not all the repeated points will be
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added to the junction point matrix. It will be verified for the presence of the
junction point in the JPM. If there exit no such entry for the junction
point identified then it will be added to the junction point matrix.
4.10 Junctional Volume Computation
The Junctional volume is the measure which represents the number of
Junction points it has in any integral image. There may be N number of
junction points present in the integral image but vary with different integral
images. Also the density of the Junction points also depend on the person to
person also it will be vary with the region also. The method counts the
number of junction points present in each regional image ie integral image
and computes the number of gray scale values participated in the junction
point selection. Based on these feature values the Junctional volume
computation is calculated.
4.11 Algorithm of Junctional Volume Computation
Input : Junction Point Matrix JPM set,
Step1 : Initialize Junctional volume matrix JVM, Mean
Junctional Volume MJV, Junction point count JPC.
Step2 : for each integral image Imgi
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∫
∫
Identify set of junction points identified.
Junction point set
JPS = ∑ ( ) ∈ ( ).
Junction point count JPC= sizeof (JPS).
Count number of positive pixels from integral image Imgi.
PP = × ∑ ( )
Compute Junctional volume JV. JV =
× ×
JVM(i) = ∑ +
End
Step3 : Compute mean of Junctional volume
mjv = ∑ ( )
( )
Step4 : Stop.
4.12 Spacial Volume Computation
Spacial volume is the measure which represents the special
distribution of junction points and is computed based on number of
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junction point and the number of pixels supports identifying the junction
points. The number of coordinate‘s lies on the boundary is identified. Based
on identified boundary points, the volumes of space occupied by the
boundary points are computed. Computed special volume is used to
compute the cumulative weight to identify the closure of the PV image
submitted.
Algorithm
Input : PV matrix Pvm, Junction Point Matrix JVM
Output: Cumulative spatial weight.
Step1 : Identify the integral images at the boundary
Step2 : Generate the region of interest image ROI image.
Step3 : find the integral images belongs to the ROI image.
Step4 : for each PV matrix pvm
Identify the coordinates at the boundary.
Coordinate matrix
cm = ∑ ( , ) + ∫ ( , ) <> ( , max))
End
Step5 : Compute the volume occupied
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sp = ∫ (∑ ( ))
Step6 : Cumulative spatial weight
CSW =
Step7 : Stop.
4.13 Trustworthy Measure Computation:
The trustworthy measure is computed using the spatial volume and
Junctional volume. The calculated measure of trustworthy could be used to
identify the person identification. The trustworthy measure shows the
closeness of the submitted PV image with the available VP in the training
set.
Algorithm
Input : Spatial volume SV, Junctional volume JV.
Output: Trustworthy measure Tm.
Step1 : Initialize trustworthy measure tm= 0.
Step2 : Compute tm = SV×JV
Step3 : Stop.
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4.14 Summary
A multi variant volumetric measure to perform PV recognition is
proposed. The method normalizes the image by resizing the image and
applies wavelet transform to increase the signal levels. The
transformed image is used to generate number of integral image and for
each integral image the set of Junctional points and their coordinates are
identified. The identified features are presented as PV matrix and using them
the Junction volume and special volume to compute the trustworthy measure
of the PV given are computed. The method produces efficient results in the
false acceptance rate by reducing the rate. Moreover, it improves the
accuracy of PV identification and authentication. The method reduces the
overall time complexity which is higher in other approaches.
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CHAPTER 5
MULTI LEVEL DORSAL-DEEP VP BASED PV
AUTHENTICATION USING WAVELET TRANSFORM
5.1 Introduction
PV authentication has become more popular where the requirement of
person identification needs more secure procedures. The environment like
secure storage systems of banking or any organization needs access
control with more complicated and secure authentication process. The smart
card systems, finger prints, facial identification has more impact to actions
of malformed. The earlier methods like person recognition suffer with the
problem of malformed intrusion which could be performed by producing
fake finger prints or facial mask and so on. To overcome the issue present in
the earlier methods of person identification and person authentication, an
alternate solution emerged as the palm VP where the palm VP of each
human is distinct but has set of features as common.
How the palm VP could be used for person identification is,
the person has to keep his hands over a sensor for few seconds. The sensor
captures the palm VP using the ultrasonic scanner attached with the system.
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The scanner sends near ultrasonic rays over the hand which is reflected
by the human veins. The muscles of the human hand absorb the rays and
the veins reflect the rays. The reflected rays are captured and produced
as a black pixel and white pixel representing remaining regions. The
scanner returns a gray scale image which is the palm VP of the human hand
placed over the scanner.
Figure 5.1 Displays the Abstract VP
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The figure 5.1 shows the generic VP of human palm and it has
superfacial veins and deep veins, the palm VP can be classified into two
layers of veins namely superfacial extremity veins and deep veins.
The Dorsal deep veins are the arteries which forms the
vanaecomitantes. There will be two veins at the sides of arteries and they
are connected by means of short branches. These volar digital veins join
to the metacarpal veins and volar venous arches. Perforating branches of the
dorsal metacarpal veins receive from the volar metacarpal veins and end
in the radial veins and in the superficial veins on the dorsum of the wrist.
With this idea of the palm VPs the recognition process has to be
designed in efficient manner. There are many approaches has been designed
for PV recognition. The junction points, shapes of palm is heavily used. The
method which uses only the junction point counts the number of points
available in the PV image. This kind of approach has poor
detection accuracy because the number of points present in the image is
depend on the scanner quality and the way the user keeps his hands over
the scanner. Both of them affects the accuracy of the authentication
system and cannot be used. In case of shape feature based methods, the
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hands and their shapes are considered by the components. Here the user
may place his hands in any angle which produces rotation in the image.
The method has to handle the rotational operation and has to transform the
image to particular angle. To perform this method has to find out the angle
of rotation the person did and then the rotation of captured image has to
be done. This is a most complicated approach and need more time to
perform authentication also produces poor results.
PV Recognition Based on Three Local Invariant Feature Extraction
Algorithms Biometric Recognition [1], In contrast to minutiae features;
local invariant features extracted from infrared PV have properties of scale,
translation property and rotation invariance property. To determine
how they can be best used for PV recognition system, here conducted a
comprehensive comparative study of three local invariant feature extraction
algorithms: SIFT - Scale Invariant Feature Transform, SURF - Speeded-Up
Robust Features and ASIFT – Affine SIFT for recognition of PV. First, the
images were preprocessed through equalization of histogram, then in total of
three algorithms were used to extract local features and the results were
obtained by comparing the distance of Euclidean. Good performances on
our own database and PolyU multispectral palm print database are
119
achieved by the experiments.
The earlier methods consider the top layer of the image where only
the dorsal veins present and they never consider about the deep veins which
are the most important factor affects the authentication process. The scanner
has to be efficient and well effective to capture the low level reflections to
produce the efficient PV image. Usually the scanner finds and picks the
reflected rays from the human veins and ignores the rays which are below
certain level. This makes the missing rays and could not produce the deep
VPs.
To perform more accurate recognition on palm VPs the scanner has to
be designed well enough to capture the low level signals and has to produce
black pixels in the image. Also the scanner has to produce the black pixels
with gray values according to the strength of the rays received from the
veins of palm.
This makes the recognition process to be performed in more efficient
manner. How it could be adapted and modified is using the wavelet
transform component with the scanner. Generally the scanner has the sensor
and transmitter and receiver. The ultrasonic waves passed through the hands
with certain strength by the transformer and the receiver receives the
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reflected rays. A new design of scanner which sends the ultrasonic rays
with little more strength than general one and has a wavelet transformation
component at the behind the receiver is proposed. Whatever the signals or
rays received will be given to the wavelet transformation component and
it performs strengthening process which boosts the signal level and gives
back. The scanner generates the black pixel at the image based on the signal
level. Here the scanner has to keep two or more level of gray values where
certain range of signals or ray will be produced with high level gray
values and the next level rays will be produced with next level gray
values.
This modification in the scanner could help us in capturing the
reflections from the deep veins which makes the PV recognition as more
meaningful one. With this idea a novel Dorsal-Deep VP Based Vein-
Artery Measure Based PV authentication approach is proposed.
5.2 Overview of Dorsal-Deep VP Based Approach
Unlike other approaches the method considers both dorsal and deep
VPs for the recognition of PV images. The method reads the input image
and applies Gabor filter which removes the noise from the image.
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The noise removed image is applied with histogram equalization
technique which enhances the input image quality. The image which is
improved on its quality is applied with wavelet analysis which splits higher
and lower signals to produce two different images where the dorsal VP is in
the higher order image and the lower order image represent the deep VPs.
The two images are split into number of small images and their
features are extracted to identify the junction points. The extracted junction
points are stored in a dorsal junction matrix and deep junction matrix. Based
on generated two matrixes to compute the depth measure of dorsal vein and
deep vein. Using these two measures a cumulative depth is computed
based on which the person identification is performed.
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Figure 5.2 Proposed System Architecture-II
123
The figure 5.2 shows the general architecture of proposed Dorsal-
Deep VP based PV classification approach. All the functional
components are explained in detail in this section.
5.3 Noise Removal
To perform noise removal in the input image, the popular Gabor
filter is applied. The image presentation based on Gabor function constitutes
an excellent local and Multi scale decomposition in terms of logons that are
simultaneously (and optimally) localization in space and domain of
frequency. Gabor functions (Multi scale filtering schemes had frames
frequently) are often used in current models of image representation in the
visual cortex because they are a good approximation to the receptive fields
of cortical cells which are simple. Anyway, Gabor functions are not
orthogonal, as a consequence the mostly used Gabor expansion is
computationally expensive, having unusual dual basis functions. The
reconstruction needs the use among iterative algorithms, neural networks or
the inversion of large matrices.
A Gabor filter is a linear filter whose impulse response is defined by
a harmonic function multiplied by a Gaussian function. Because of the
property named multiplication-convolution, the term Convolution theorem
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can also be said. The Gabor filter's Fourier transform impulse response is
the convolution of the Fourier transform of the harmonic function and the
Fourier transform of the Gaussian function.
5.4 Histogram Equalization
The next step is to enhance the quality of input PV image. The image
enhancement is performed using histogram equalization technique. 64 bit
histogram which will be used for further processing is generated. First the
set of possible intensity values between 0 and 256 are generated.
Computation takes place for each value of the set to compute set of pixels
comes with the grayscale value, the number of pixel with the same gray
scale values are computed. Then for each pixel, the round of operation to
equalize the values with the neighboring pixels based on computed
probability distribution is performed. This increases the image quality and
helps to identify the junction points in the next levels.
Algorithm of Histogram Equalization:
Input : Noise Removed image Img.
Output: Equalized Enhanced image Eimg.
Step1 : start
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Step2 : read input image Img.
Step3 : Ivset={0…256} (load possible intensity values).
Step4 : for each value in Ivset
Compute tp = total pixel intensity value Ivset(i) / total no of pixels.
End.
Step5 : for each pixel p in image Img
Perform transformation by rounding the Intensity values nearer. T(k) =
round(L-1) Σn=0-kpn
Compute probability distribution.
Pn – probability distribution
End
Step 6: stop.
5.5 Wavelet Analysis
The wavelet analysis is performed in the enhanced image to
separate the low energy pixels from higher energy pixels. The method
does not consider the gray values less than 100 and identifies the pixels
which are greater than 200 and which are between 100 and 200. The
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wavelet analysis is performed to identify the low energy pixels and higher
energy pixel. The method generates two set of binary images and initialize
the pixels of the image as zero. For each pixel from the equalized
image the method identifies the pixel which is coming in the range of
gray values. The identified pixels and their index in the binary image is
triggered on and left the rest of all pixels in the off mode. Similarly the
same is performed by the method for the low level energy pixels and
generates deep vein image.
Algorithm of Wavelet Analysis:
Input : Enhanced Image Img
Output: Dosar vein image Domg, Deep Vein Image Dmg.
Step1 : Read input image
Initialize DT - Dosar Tolerance, DeT - Deep Tolerance.
Step2 : for each pixel Pi from Img energy of pixel needs to compute
Er = GrayValue(Pi(Img)) If Er ± DT then Domg(pi)=1.
Else if Er ± DeT then Dmg(pi)=1.
End
End.
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Step3 : stop.
5.6 Sub-Sampling Image Generation
The sub sampling image is a set of tiny images produced from the
original image given. In our approach, the deep vein image and the
dosarimage generated from the wavelet analysis is used to produce the sub-
sampling image. The method always has a fixed box size based on which
the input image is split into N number of images. The generated images are
used to produce the junction points present in the image.
Pseudo Code:
Input : Image IMG.
Output: Sub-sampled image set IIS.
Step1 : initialize box size M.
Step2 : while (M×M)<sizeof(IMG) Generate image
I = ∫ ( × ) IIS = ∑I(IIS)+I.
End.
Step3 : Stop.
5.7 Junction Point Computation
Generated subsample image set is used to compute the junction points
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130
∫
present in each of the deep vein image and dosar vein images. There are
set of sub sampled image for each image and for each of the sub image from
the set given the presence of junction points are identified. For each pixel
which is in positive value, for the presence of minimum positive occurrence
in the eight neighbors are checked. There must be minimum of two positive
values to be present to conclude the presence of junction point. The
method maintains the junction point matrix where it stores the details of
junction point identified on each of the image. The method stores the
number of the junction point, and number of positive neighbor pixels are
found and so on.
Algorithm:
Input : Dosar Image Dimg, Deep vein image Dvimg.
Output: Dosar Junction matrix Djm, Deep junction matrix Dejm.
Step1 : Initialize Djm, Dejm.
Step2 : for each pixel Pi from Dimg
K= ∑( ( ) == 1),1,0)
If k>1 then
If Pi then
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131
∫
Djm(i) = {Pi, K}
End
End.
Step3 : for each pixel Pj from Dvimg
L= ∑( ( ) == 1), 1,0)
If L>1 then
If Pj then
Dejm(i) = {Pi, K}
End
End.
Step4 : Stop.
5.8 Dorsal Depth Measure
The dorsal depth measure shows the cumulative density of junction
points present in the all the sub sampled regions. For each dorsal image,
junction point matrix has number of junction point has been identified and
computation takes to identify how many junctions has present for each
junction point and so on. Based on these values, the dorsal depth measure is
computed. The weight of junction points present in the super facial VPs is
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132
∫
represented by dorsal depth.
Algorithm:
Input : Dorsal Junction Point matrix DJPM set DPMS.
Output: Dorsal depth measure DDM.
Step1 : for each junction point matrix Djpm from DPMS
Compute number of junction point
Njp = ∫ ∑ ( )! =
Compute junction density measure JD.
JD= ∑ ( )
DDM= ∑DDMi+JD
End
Step2 : Compute dorsal depth measure
DDM = ∑ ( )
( )
Step3 : Stop.
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133
5.9 Deep vein Depth Measure
The deep vein depth measure shows the cumulative density of
junction points present in the all the sub sampled regions of deep vein
images. The junction point matrix of each deep vein image has number of
junction point has been identified and for each junction point how many
junctions has present and so on. Based on these values, the deep vein depth
measure is computed. The weight of junction points present in the deep VPs
are represented by deep vein depth.
Algorithm:
Input : Dorsal depth measure Ddm, Deep vein depth measure Dvdm.
Output: Classification result Cr.
Step1 : From each class image from training set
Compute cumulative weight
TCw = TDdm*TDvdm.
Compute cumulative weight
CW = Ddm*Dvdm.
Compute distance between them
Ed = Euclidean(TcW,CW)
131
134
Add to distance set
Ds = ∑Edi(DS)+Ed.
End
Step2 : Compute average distance
Avd = ∑ ( )
( )
Step3 : if Avd< Threshold
Recognize as positive
Else
Recognize negative.
End.
Step4 : Stop.
5.10 PV Recognition
The PV recognition is performed based on computed deep vein depth
measure and dorsal vein depth measure. Using these two measures the
overall closure for any person based on the palm VP are computed. The
computed cumulative weight represents the closure of the PV
submitted with the trained set.
132
135
Algorithm:
Input : Dorsal depth measure Ddm, Deep vein depth measure Dvdm.
Output: Classification result Cr.
Step1 : From each class image from training set
Compute cumulative weight
TCw = TDdm*TDvdm.
Compute cumulative weight
CW = Ddm*Dvdm.
Compute distance between them
Ed = Euclidean(TcW,CW)
Add to distance set
Ds = ∑Edi(DS)+Ed.
End
Step2 : Compute average distance
Avd = ∑ ( )
( )
Step3 : if Avd< Threshold
Recognize as positive
133
136
Else
Recognize negative.
End.
Step4 : Stop.
5.11 Summary
A multi-level dorsal-deep VP based PV recognition approach is
proposed. The method removes the noise and performs histogram
equalization to enhance the image. The enhanced image is applied
with wavelet analysis and splits the higher order and lower order VP.
Generated two different images are split into sub sample images and
their junction points are identified. Identified junction point matrix is used
to compute the dorsal depth and deep vein depth measure to compute
the cumulative weight. Based on cumulative weight an average
distance measure is computed to identify the person base on some
threshold value. The proposed method has produced efficient results and
reduces the false ratio and time complexity.
134
137
CHAPTER 6
RESULTS AND DISCUSSION
A PV recognition approach has various strategic impacts in biometric
authentication systems and to improve the efficiency and to provide more
strategic solutions, different approaches at different levels are
proposed. This chapter discussed about the experimental analysis and results
produced by them. Two different approaches namely ―Multi-Variant
Volumetric Measure on Upper Extremity VP Based PV Recognition
Using Wavelet Transform‖ and ―Multi Level Dorsal-Deep VP Based
PV authentication Using Wavelet Transform‖ is proposed and results
produced by these approaches are discussed in detail in this chapter.
Table 6.1 Details of Data Set Being Used
Parameter
Value
Number of Classes 250
Number of samples per class 10
Total Number of samples 2500
Size of training set 70 percent
Size of testing set 30 percent
135
138
The table 6.1 shows the details of data set being used to evaluate the
performance of the proposed method. It specifies that 250 classes of
samples where each class has 10 numbers of images have used.
6.1 Multi-Variant Volumetric Measure on Upper Extremity VP
Based PV Recognition Using Wavelet Transform
In this method, the captured image is resized to a fixed size and
applied with wavelet transform. The transformed image is applied with
edge detection process to get the concrete VPs. The edge detected image is
then split into number of integral images and for each integral image
generated a number of junction points are identified with their co-ordinates.
With the number of interest points and their co-ordinates, the volumetric
measure is computed. Using all these details, finally the trustworthy of the
user for each class is computed and verified.
136
139
Figure 6.1 Snapshot of Input PV Image Selected
The figure 6.1 shows the snapshot of selected input image to perform
authentication and a right hand with inclined image which has to be
rotated to get the features of the palm VP are selected. It will be further
used in boundry marking process.
137
140
Figure 6.2 Snapshot of Boundary Marked
The figure 6.2 shows the boundary of the PV identified and marked.
From the figure 6.2, the second image named as Boundary image shows the
boundary points marked in red color lines and the corner points are marked
with green stars. The process of extraction will be carried out in the marked
region.
138
141
Figure 6.3 Rotated Snapshot of PV Image and the Region Marked to be
Extracted
The figure 6.3 shows the region marked with blue lines which is the
region of interest to be extracted from the PV image for processing. It
shows clearly that the portion has been marked and ready for feature
extraction. The region marked gets rotated for its next step.
139
140
Figure 6.4 Snapshot of Extracted Region of Interest
The figure 6.4 shows the snapshot of PV region being extracted from
the input image and it shows clearly that the portion marked in the previous
stage has been extracted for further processing. This extracted region had
some unwanted signals on it and it should be removed by noise removing
technique.
141
Figure 6.5 Snapshot of Noise Removed Image
The figure 6.5 shows the snapshot of image which is removed from
noise present in the ROI image. It shows that the image has been removed
from the noise present in the image. Eventhough noise get removed, it still
required to remove background for further proceedings.
142
Figure 6.6 Snapshot After Background Removal
The figure 6.6 shows the Region of interest image after
performing the background subtraction process and the background
subtraction has been performed by binary imaging technique. After the
background removal it can not be used directly for skeleton identification
process, to do so the background removed image should be normalized.
143
Figure 6.7 Snapshot of Normalized PV Image
The figure 6.7 shows the snapshot of normalized PV image which
will be used to perform skeleton identification process. The
normalized image has veins with larger dimensions and so that the veins
skeleton has to be identified to perform junction point identification and to
perform other computation process.
144
Figure 6.8 Snapshot of Skeleton Identified Image
The figure 6.8 shows the skeleton generated PV image. It
shows clearly that the proposed method has produced efficient skeleton
of PV image. It shows the collection of images such as input image,
boundary image, vein region image, region extracted image, filtered image,
background removed image, extracted image and skeleton image.
145
Figure 6.9 Snapshots of Identified Junction Points in the Image
The Figure 6.9 shows the details of junction points has been identified
from the given input image. It shows that there are number of points has
been identified which will be used to compute the volumetric measure
between the points. The result of PV matching or not matching is obtained
by the values of volumetric measure between the points.
146
Figure 6.10 Snapshot of PV Image Matched
The figure 6.10 shows the PV image gets matched with the proposed
method. The skeleton image is used to match the PV image. After matching
the PV image, the step by step result of proposed method can be drawn.
147
Figure 6.11 Snapshot of Step by Step Result of Proposed Method
The figure 6.11 shows the snapshot of result produced by each step at
one shot. It shows clearly that the proposed method has produced efficient
results. From the above discussions presented, the proposed method has
been presented with various stages of results. The method has identified
various junction points from the input image and has computed volumetric
148
measure between all the junction points. By calculating the volumetric
measure between all the measures a cumulative special volume has been
computed. Computed special volume has been used to compute trustworthy
measure of the given input image to perform authentication of the PV
image.
149
Graph 6.1 Comparison of PV authentication Accuracy
The graph 6.1 shows the comparison of authentication
accuracy produced by different methods. It shows clearly that the
proposed method has higher accuracy than other methods. The proposed
(Multi-Varient) method reaches 96% of accuracy where LBP/LDP, SIFT,
PCA/ANN had lesser accuracy than proposed method.
150
Graph 6.2 Comparison of False Positive Ratio of Different Methods.
The graph 6.2 shows the comparison of false result produced
by different methods. It shows clearly that the proposed method has
produced less false results than other methods. The proposed (Multi-
Varient) method only produces 4% of the false rate which is lesser than the
other methods.
151
Graph 6.3 Comparison of Time Complexity of Different Methods.
The graph 6.3 shows the comparison of time complexity produced by
different methods on varying number of samples and classes. It proves
clearly that the proposed (Multi-Varient) method has produced less time
complexity than other methods at different number of classes.
152
Table 6.2 Comparison of Resilience, Rotation and Noise
Method
Translation
Rotation
Noise
Local Binary No Yes No
SIFT No Yes No
PCA No No Yes
Proposed Yes Yes Yes
The table 6.2 shows the comparison of resilience, rotation and noise
factors considered in different approaches. Unlike other approaches, the
proposed method has considered all the factors in performing PV
recognition.
6.2 Multi-Level Dorsal-Deep VP Based PV authentication Using
Wavelet Transform
The method reads the input image and applies Gabor filter which
removes the noise from the image. The noise removed image is applied with
histogram equalization technique which enhances the input image quality.
The quality improved image is applied with wavelet analysis which splits
higher and lower signals to produce two different images where the dorsal
VP is in the higher order image and the lower order image represent the
deep VPs. The two images are divided into number of small images and
153
their features are extracted to identify the junction points. The extracted
junction points are stored in a dorsal junction matrix and deep
junction matrix. Based on generated two matrixes the dorsal vein depth
measure and deep vein depth measure are computed. Using these two
measures a cumulative depth is computed based on which the person
identification is performed.
Figure 6.12 Snapshot of Input Image Selected for PV Recognition
154
The figure 6.12 shows the snapshot of the PV image selected
to perform PV recognition and the image selected is displayed in the first
axes component of the mat lab GUI component. This input image can be
further extracted for next stage of process.
Figure 6.13 Snapshot of Region Extracted
The figure 6.13 shows the snapshot of region of interest being
extracted from the input image which will be used to perform histogram
155
equalization and identifying junction point identification.The extracted
image can be further processed for equalization in terms of histogram to
produce histogram equalized ROI image.
Figure 6.14 Snapshot of Histogram Equalized ROI Image
The figure 6.14 shows the snapshot of histogram equalized image
which is performed on the region of interest image. This histogram
equalized ROI image can be obtained by the extracted region of input
156
image. This obtained image can be further taken for background subtraction
in next process.
Figure 6.15 Snapshot of Background Subtraction
The figure 6.15 shows the snapshot of background subtraction image
obtained from the histogram equalized image. It shows that the white
region is the feature considered to perform edge detection and junction point
157
identification. After the process of background subtraction, the image can
be proceeded for normalizing process.
Figure 6.16 Snapshot of Normalized Image
The figure 6.16 shows the snapshot of normalized image obtained
from background subtracted PV image which will be used to perform edge
detection and junction point identification.
158
Figure 6.17 Snapshot of Junction Point Identified Image
The figure 6.17 shows that the edge detected and junction point image
obtained from the normalized image and also displays the result of
the complete approach. This image is a collection of input image, region
marked image, rotated image, region of interest image, histogram equalized
image, background removed image, binarized image, junction point image
and identified palm vein image.
159
Figure 6.18 Snapshot of Identified PV Image
The figure 6.18 shows the snapshot of PV image being identified by
the proposed multi-layer approach. After the process of junction point
identification, the PV image gets matched with the existing database. This
process of matching PV image is done in lesser time and with greater
accuracy achived when comparing with existing techniques.
160
Graph 6.4 Comparison of PV authentication Accuracy
The graph6.4 shows the comparison of authentication accuracy
produced by different methods. Multi-Layered is nothing but a proposed
method and is compared with other existing methods such as LBP/LDP,
SIFT, PCA/ANN. It shows clearly that the proposed method has higher
accuracy than other methods.
161
Graph 6.5 Comparison of False Positive Ratio of Different Methods.
The graph 6.5 shows the comparison of false result produced
by various methods. It shows clearly that the proposed method has
produced less false results than other methods. The proposed Multi-Layered
method is well achived in lesser false ratio had only value of 4%.
162
Graph 6.6 Comparison of Time Complexity of Different Methods.
The graph 6.6 shows the comparison of time complexity produced by
different methods on varying number of samples and classes. It shows
clearly that the proposed method has produced less time complexity than
other methods at different number of classes. Here Multi-Layered proposed
method is compared with PCA/ANN and SIFT methods. The proposed
method shows clearly that it takes only lesser time for any number of
samples.
163
6.3 Comparative Analysis
Two approaches for building of PV authentication technology
are proposed and each has been tested with different number of classes and
samples and produced efficient results in all the factors of quality of PV
recognition and authentication. Here the proposed two methods are Multi-
Varient and Multi-Layered. It is clear that both the proposed method have
more accuracy than other existing methods such as PCA/ANN, SIFT,
LBP/LDP.
Graph 6.7 Comparison of PV authentication Accuracy
164
The graph 6.7 shows the comparison of PV authentication accuracy
produced by different methods and it shows that the Multi-Layered and
Multi-Varient methods have produced efficient accuracy than the other
methods at different number of classes and samples.
165
CHAPTER 7
CONCLUSION AND FUTURE WORK
The appropriate image segmentation technique on VP is applied.
Two different approaches namely ―Multi-Variant Volumetric Measure on
Upper Extremity VP Based PV Recognition Using Wavelet Transform‖
and ―Multi Level Dorsal-Deep VP Based PV authentication Using
Wavelet Transform‖ is proposed.
The proposed multi variant volumetric measure method normalizes
the image by resizing the image and applies wavelet transform to increase
the signal levels. The transformed image is used to generate number of
integral image and for each integral image should identify the set of
Junctional points and their coordinates. The identified features are presented
as PV matrix and it compute the Junctional volume and special volume to
compute the trustworthy measure of the PV given. The method produces
efficient results in the false acceptance rate by minimizing the rate. Also it
improves the accuracy of PV identification and authentication. The method
reduces the overall time complexity which is higher in other approaches.
The proposed multi-level dorsal-deep VP based PV recognition
approach removes the noise and performs histogram equalization to enhance
166
the image. The enhanced image is applied with wavelet analysis and
splits the higher order and lower order VP. Generated two different
images are split into sub sample images and their junction points are
identified. Identified junction point matrix is used to compute the dorsal
depthness and deep vein depthness measure to compute the cumulative
weight. Based on cumulative weight an average distance measure is
computed to identify the person base on some threshold value. The proposed
method has produced efficient results and reduces the false ratio and time
complexity.
The method reads the input image and applies Gabor filter which
removes the noise from the image. The noise removed image is applied with
histogram equalization technique which enhances the input image quality.
The quality improved image is applied with wavelet analysis which splits
higher and lower signals to produce two different images where the dorsal
VP is in the higher order image and the lower order image represent the
deep VPs. The two images are split into number of small images and their
features are extracted to identify the junction points. The extracted junction
points are stored in a dorsal junction matrix and deep junction matrix. Based
on generated two matrixes, the dorsal vein depthness measure and deep vein
167
depthness measure are computed. Using these two measures a cumulative
depthness is computed based on which the person identification
is performed.
Experimental results shows that the snapshot of region of interest
being extracted from the input image which will be used to perform
histogram equalization and identifying junction point identification.
Comparison of authentication accuracy produced by
different methods, experimental results show clearly that the proposed
method has 96% accuracy which is higher than other methods.
Comparison of false result produced by different methods, the
proposed method has 4% false which is lesser comparatively in the result of
other methods.
Time complexity produced by different methods on varying number
of samples and classes are compared and the result shows clearly that the
proposed method has produced less time complexity than other methods at
different number of classes. Experimental results show that the comparison
of resilience, rotation and noise factors considered in different approaches.
Unlike other approaches, the proposed method has considered all the factors
in performing PV recognition.
168
In the future work, pre-align up the PV image footed on the
discovered PV section and minutia ends can be identified. The same
approach can be used for combined multimodal PVs and face biometric
verification system which enhances the quality of biometrtic authentication
by extracting PV and facial features. The method combines both PV and
facial features to perform biometric authentication using similar
methodology. Bifurcation points and ending points are similar to finger
prints, these feature points are used as a geometric representation of the
shape of VPs. These geometric representation can additionaly be used for
improving security.
169
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LIST OF PUBLICATIONS
INTERNATIONAL JOURNALS
1. ―Image Pre-Processing Methods for Personal Identification‖,
Journal of computer Applications, Vol. 1, No. 4, pp.19–24, October–
December 2008.
2. “Hand Palm Vein Authentication by Using Junction Points with
Correlation Method”, International Journal Of Computational Engineering
Research, Vol. 03, Issue. 1, pp. 67-73, January 2013.
CONFERENCES
1. ―Efficient Palm print and Palm Vein Based Person Recognition
Using Junction Point with Correlation Method -An Illustration‖,
International Conference On Computer Science And Engineering, WASET
(BANGKOK, THAILAND), 25th-26th December 2011.
2. ―Palm Vein Authentication Using Wavelet Transform‖,
International Conference on Innovative Computing And Information
Processing, Mahendra Engineering College (Mallasamudram), 29th to31st
March 2012.
3. ―Multi-Variant Volumetric Measure on Upper Extremity Vein
Pattern‖, International Conference on Sustainable Approaches for Green
Computing ,Economy and Environment-SAGCEE-13, V.M.K.V Engineering
College, 09th to 11th December 2013.
190