Hand Veins Recognition System
João Ricardo Gonçalves Neves
Thesis to obtain the Master of Science Degree in
Electrical and Computer Engineering
Examination Committee
Chairperson: Prof. Fernando Duarte Nunes
Supervisor: Prof. Paulo Luís Serras Lobato Correia
Members of the Committee: Prof. Hugo Pedro Martins Carriço Proença
September 2013
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Acknowledgements
In first place, I would like to thank Professor Paulo Lobato Correia for his support,
availability, ideas and continuous guidance provided throughout the development
of this dissertation.
I also want to thank Nuno Moço for all his assistance in the programming area, and
Pedro Fernandes for his support in the conception and construction of the
prototype used in the thesis.
Without the contribution of my family and friends, it would have been impossible
to have so many hand veins acquisitions available in the database, and for that I am
really thankful.
I want to express my gratitude to my family, for the all the patience, guidance and
support provided in the last years: To my father who was always available to
provide new useful ideas that might improve my thesis; to my mother, who was
always available to listen to my complaints and to give advice; and to my brother
who was always there to provide his help.
I also want to thank my girlfriend Mariana Cadete for all her motivation, support
and patience through all the superior education years.
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Abstract
Accurate protection systems capable of replacing the traditional passwords and ID
cards are essential, for commodity and for security reasons. A hand-vein pattern
recognition system is just one of a vast group of biometric techniques under
research, in order to become the reference recognition system.
This dissertation presents a hand vein biometric recognition system that uses the
hand blood vessels pattern to identify an individual. All biometric systems have an
immense application potential, as they present advantages over the traditional
identification systems. They are able to work with patterns that are very hard to
duplicate, since they are different from person to person, and it is also impossible
to lose or forget them, since the biometric characteristics are intrinsically attached
to the human body.
The developed approach was created with the intent of providing an effective
protection system despite having been designed and implemented using
inexpensive hardware, in comparison with the biometric recognition systems
presently offered at a commercial level.
The results show that a reliable system can be produced at a low cost and can be
used standalone or in combination with other systems.
Keywords
Hand-based biometrics, biometrics recognition, palm veins, hand geometry, palm vein
acquisition system, palmprint, web-camera.
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Resumo
A necessidade de sistemas de identificação eficientes e de baixo custo aumenta à
medida que as transações de dados pessoais ou valores por via eletrónica também
aumentam. O sistema de reconhecimento de padrões de veias desenvolvido nesta
dissertação é apenas um de um vasto grupo de técnicas de reconhecimento
biométrico que estão a ser exploradas presentemente. Estes sistemas estão a ser
desenvolvidos com o objetivo de se tornarem os métodos de proteção de
referência, superiores aos métodos de proteção tradicionais baseados em palavras-
chave e em cartões de identificação.
Os sistemas biométricos têm imenso potencial porque oferecem vantagens em
relação aos sistemas de proteção convencionais, por utilizarem padrões
intrínsecos do utilizador, sendo por isso muito difíceis de duplicar. A
impossibilidade de se poderem perder ou de serem esquecidos são outras grandes
vantagens dos sistemas de reconhecimento que utilizam as características
biométricas.
O sistema desenvolvido pode ser utilizado isoladamente ou combinado com outros
sistemas. Foi desenhado para ter um custo de Hardware muito baixo em relação
aos sistemas que atualmente existem no mercado. Apesar da diferença de custo, os
resultados mostram que é suficientemente fiável para poder ser utilizado em
aplicações reais.
Palavras-chave
Reconhecimento biométrico, Biometria baseada na mão, Veias da palma da mão, Web-
Camera, Geometria da mão.
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Table of Contents
1. Introduction ................................................................................................................. 1
1.1. Biometric Systems on the Market ..................................................................... 2
1.1.1. Finger Vein Recognition System ............................................................ 2
1.1.2. Iris Recognition System .......................................................................... 6
1.2. Objectives of the Dissertation ........................................................................... 9
1.3. Contributions of the Dissertation .................................................................... 10
1.4. Structure of the Dissertation ........................................................................... 10
2. Biometric Systems .................................................................................................... 13
2.1. Biometrics Overview ........................................................................................ 13
2.1.1. Social Acceptance and Privacy Issues ................................................. 15
2.1.2. Architecture of a Biometric Recognition System ............................... 16
2.1.3. Performance Evaluation ....................................................................... 17
2.1.4. Comparison between biometric systems ............................................ 19
2.2. Traits used in Hand Recognition Techniques ................................................... 20
2.2.1. Recognition Based on Hand Vein Patterns ......................................... 21
2.2.2. Recognition Based on Hand Palmprints ............................................. 21
2.2.3. Recognition Based on Hand Geometry ................................................ 22
3. State of the Art ........................................................................................................... 23
3.1. Image Acquisition............................................................................................. 23
3.2. Preprocessing ................................................................................................... 27
3.3. Feature Extraction............................................................................................ 31
3.4. Matching .......................................................................................................... 33
4. Proposed Biometric Identification System ............................................................. 37
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4.1. Potential Applications ...................................................................................... 37
4.2. System Architecture ......................................................................................... 38
4.2.1. Image Acquisition System .................................................................... 39
4.2.2. Preprocessing ........................................................................................ 42
4.2.3. Feature Extraction ................................................................................ 49
4.2.4. Matching System ................................................................................... 53
5. User Interface ............................................................................................................ 55
6. Experimental Results ................................................................................................ 61
6.1. Database Creation ........................................................................................... 61
6.2. Performance Evaluation .................................................................................. 61
6.3. Operating Point Selection ................................................................................ 66
7. Plans for the Future .................................................................................................. 69
8. Conclusions ................................................................................................................ 71
9. References .................................................................................................................. 73
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List of Figures
FIGURE 1 - FINGER VEIN SYSTEM DEVELOPED BY HITACHI THAT IS BEING USED IN POLAND [9]. ... 3
FIGURE 2 - HITACHI USB FINGER READER [4]. ..................................................................................... 3
FIGURE 3 - FINGER VEIN CAPTURE METHOD [4]. ................................................................................. 4
FIGURE 4 - ILLUMINATION TECHNIQUE [4]. .......................................................................................... 5
FIGURE 5 - BLOCK DIAGRAM OF FINGER VEIN AUTHENTICATION [4]. ................................................. 5
FIGURE 6 - HUMAN EYE [42]. .................................................................................................................. 7
FIGURE 7 - AOPTIX TECHNOLOGIES INSIGHT® DUO [10] .................................................................. 8
FIGURE 8 - VB I-MATCH DEVELOPED BY VISUAL BOX. [18] ............................................................. 8
FIGURE 9 - DEVELOPED ASSEMBLY. ........................................................................................................ 9
FIGURE 10 - BIOMETRIC SYSTEM MAIN MODULES. ..............................................................................17
FIGURE 11 – EXAMPLE OF FAR AND FRR FOR DIFFERENT THRESHOLD VALUES............................18
FIGURE 12 – EXAMPLE OF ROC CURVE ...............................................................................................19
FIGURE 13 – FAR, FRR AND FTE VALUES FOR DIFFERENT BIOMETRIC TECHNIQUES [4].............20
FIGURE 14 - THE LINES PATTERN OF THE PALMPRINT. 1-HEART LINES, 2-HEAD LINE, AND 3-LIFE
LINE.[19] ........................................................................................................................................22
FIGURE 15 - TYPICAL RECOGNITION SYSTEM ARCHITECTURE ..........................................................23
FIGURE 16 – IMAGE ACQUISITION SETUP FOR THE REFLECTION APPROACH (LEFT) AND
TRANSMISSION APPROACH (RIGHT)[1]. ......................................................................................23
FIGURE 17 - INFRARED PALM IMAGES CAPTURED BY THE REFLECTION (LEFT) AND TRANSMISSION
(RIGHT) METHODS [1]. .................................................................................................................24
FIGURE 18 – MODRIS ET AL EXPERIMENTAL SETUP OF PALM VEIN INFRARED IMAGE ACQUISITION
[1]. ..................................................................................................................................................25
FIGURE 19 - HUAN ZHANG ET AL. HARDWARE SETUP [2]. .................................................................27
FIGURE 20 – OBTAINED ROI AREA WITH MAURICIO RAMALHO APPROACH [17]. .........................28
FIGURE 21 - HUAN ET AL. ACQUIRED IMAGE WITH THE INSCRIBED CIRCLE [2]. ..............................29
FIGURE 22 – NORMALIZED ROI THAT WILL BE USED IN THE FEATURE EXTRACTION MODULE [2].
.........................................................................................................................................................30
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FIGURE 23 – (A) THE ORIGINAL PALMPRINT, (B) PALM PRINT AFTER THE CONTRAST ADJUSTMENT
AND SMOOTHING FILTERS [43]. ...................................................................................................30
FIGURE 24 – FEATURE EXTRACTION USING OLOF AT [24]. .............................................................32
FIGURE 25 – FINAL PREPROCESSING STAGES. (A) TARGET IMAGE. (B) BINARIZED IMAGE. (C)
FILTERED IMAGE. (D) NOISE ELIMINATION. (E) THINNED IMAGE. (F) REPAIRED IMAGE [2].
.........................................................................................................................................................33
FIGURE 26 - DEVELOPED SYSTEM ARCHITECTURE ..............................................................................38
FIGURE 27 - DEVELOPED PALM VEIN PATTERN ACQUISITION ASSEMBLY. ........................................39
FIGURE 28 - SYSTEM ILLUMINATION. ...................................................................................................40
FIGURE 29 – SQUARE INFRARED FILTER THAT NEEDS TO BE REMOVED IN ORDER FOR THE
WEBCAM TO CAPTURE INFRARED IMAGES. ..................................................................................41
FIGURE 30 - PHOTOGRAPHIC FILM USED TO FILTER OUT VISIBLE LIGHT. .........................................41
FIGURE 31 - MODIFIED WEB CAMERA USED TO DO HAND VEINS ACQUISITION.................................42
FIGURE 32 - DEVELOPED PREPROCESSING STAGES. ............................................................................42
FIGURE 33 - RAW IMAGE AND IMAGE AFTER ADJUSTMENT STEP. ......................................................43
FIGURE 34 - IMAGE SMOOTHED BY A WIENER FILTER. .......................................................................43
FIGURE 35 - HAND SEGMENTED IN FOREGROUND AND BACKGROUND. .............................................44
FIGURE 36 - HAND CONTOUR. ...............................................................................................................44
FIGURE 37 -ELLIPSE WITH THE SAME NORMALIZED SECOND CENTRAL MOMENT AS THE HAND
REGION. ...........................................................................................................................................45
FIGURE 38 - FIXED POINT MARKED AS THE HALF RED CROSS. ............................................................46
FIGURE 39 - HAND REFERENCE POINTS................................................................................................46
FIGURE 40 - REGION OF INTEREST ACQUISITION. ................................................................................47
FIGURE 41 - ROI TREATMENT STEPS. ..................................................................................................48
FIGURE 42 - REFERENCE POINTS USED TO CALCULATE THE HAND GEOMETRY CHARACTERISTICS
VALUES. ...........................................................................................................................................49
FIGURE 43- OLOF OUTPUT IN THE THREE DIRECTIONS, AND WITH SCALE
RATIO EQUAL TO 3. ........................................................................................................................50
FIGURE 44 - OLOF OUTPUT IN THE THREE DIRECTIONS, Θ=Π/6, Θ=Π/3 AND Θ=0 WITH SCALE
RATIO EQUAL TO 2. ........................................................................................................................50
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FIGURE 45 - FAR VALUES FOR DIFFERENT SCALE RATIOS. .................................................................51
FIGURE 46 - FRR VALUES FOR DIFFERENT SCALE RATIOS. .................................................................51
FIGURE 47 - FAR VERSUS FRR FOR DIFFERENT TEMPLATE SIZES. ...................................................52
FIGURE 48 - DATABASE EXAMPLE. ........................................................................................................52
FIGURE 49 - INITIAL INTERFACE MENU ...............................................................................................56
FIGURE 50 - ENROLL INTERFACE ..........................................................................................................56
FIGURE 51 - CORRECT HAND PLACEMENT ..........................................................................................57
FIGURE 52 –THREE POSSIBLE HAND CONTOURS. ................................................................................58
FIGURE 53 - EXAMPLE OF A BAD IMAGE ACQUISITION. .......................................................................58
FIGURE 54 - MATCHING RESULT INTERFACE ......................................................................................59
FIGURE 55 - DATABASE CONTROL INTERFACE AFTER DETECTING AN ERROR. ................................60
FIGURE 56 - DATABASE CONTROL INTERFACE AFTER NOT DETECTING ANY ERROR. ......................60
FIGURE 57 - RECEIVER OPERATION CHARACTERISTIC CURVE FOR A ROI WITH 128X128 PIXELS.
.........................................................................................................................................................63
FIGURE 58 - FAR AND FRR AT DIFFERENT OPERATING THRESHOLDS. ............................................64
FIGURE 59 - ROC CURVE FOR DIFFERENT ROI DIMENSIONS. ............................................................65
FIGURE 60 - FRR (%) AGAINST FAR (%) TO OBTAIN EER FOR DIFFERENT ROI DIMENSIONS. .65
FIGURE 61 - HAND TEXTURE RECOGNITION SYSTEM USING A REGULAR LAPTOP COMPUTER
CAMERA. ..........................................................................................................................................69
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List of Tables
TABLE 1 - COMPARISONS BETWEEN THE FRR, FAR AND FTE OF DIFFERENT BIOMETRIC DEVICES
[4]. ..................................................................................................................................................19
TABLE 2 - FAR, FRR AND GAR FOR DIFFERENT THRESHOLD VALUES. ...........................................62
TABLE 3 - VALUES OF FAR AND FRR FOR DIFFERENT OPERATING POINTS. ....................................66
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List of Acronyms
ATM Automated Teller Machine
CMF Complex Matched Filter
FAR False Accept Rate
FRR False Reject Rate
FTE Failed To Enroll Rate
GAR Genuine Accept Rate
HD Hamming Distance
OLOF Orthogonal Line Ordinal Features
ROC Receiver Operating Characteristic
ROI Region of Interest
XOR Bitwise Exclusive Disjunction
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1. Introduction
Nowadays, we can access our personal data from almost everywhere. This is very
convenient but entails increasing risks since the probability of phishing credentials
increases with the number of users. More sophisticated protection systems are
required to control possible harassments, such as ID cards cloning, theft or
compromised passwords.
When thinking about digital protection, one which immediately comes to mind is
the use of passwords and smart cards, since they are used daily for almost
everything. Despite being used very frequently, passwords and smart cards are a
relatively insecure method of protection and access control.
The biometric systems experienced a significant growth in the recent years, both at
research and commercial level, pushed by the need for innovative and improved
ways to protect our personal information.
The word biometric comes from the Greek words ‘bio’ (life) and ‘metric’ (to
measure). The field of biometrics recognition deals with the identification of a
human by using its distinctive traits. They can be categorized in two major groups,
behavioral and physiological.
The behavioral traits are related to the user behavior and include the signature or
gait. The physiological traits include personal characteristics like hand geometry,
fingerprints, ear or face.
The first biometric systems remount to around 29.000 BC, where the primitive
humans used handprints to sign their drawings in cave walls [39]. Much later,
around 500 BC, in Babylonia, fingerprints were used to sign business transactions.
More recently, in the end of the 19th century, Juan Vucetich, an Argentinian police
official, used the prisoners’ fingerprints to catalog Argentina’s criminals [37], and
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this technic became a standard identification system which is used all over the
world.
Biometric systems that analyze traits like the finger veins, fingerprint or iris, are
nowadays mostly used as a form of recognition. Those systems are widely used to
control the access to certain applications, private areas or even in forensic
scenarios. The majority of the systems available provide real time automatic
solutions which extract a human feature, then compute a template and compare it
with the ones previously stored in a database to provide a matching decision.
1.1. Biometric Systems on the Market
A wide range of biometric recognition systems are already available in the market
and can be found in the most varied places, like ATM machines or in Airport
passport controls. They all exploit human features to identify an individual and
their reliability is usually provided by expensive equipment.
The following subsections 1.1.1 and 1.1.2 describe three commercial systems. The
first system is unimodal and uses the finger veins as the biometric trait , the second
one is bimodal and uses the features provided by the face and iris and the third one
is also bimodal and uses the face, iris and palmprint as the biometric traits.
1.1.1. Finger Vein Recognition System
One example of a biometric system being commercially explored is the finger vein
recognition system, developed by Hitachi for application in ATM machines [4]. The
finger vein system was developed through Hitachi’s research activities in the area
of medical scanning. In 1997 while the researchers were doing studies about infant
brain activity, they found out that changes in blood flow could be examined using
high intensity near infrared light. It took around eight years of research and
development to create the commercial application. This new recognition system is
already being used in 75% of the bank branches in Japan, making it the market
leading biometric technique in that sector [4]. The users are able to withdraw
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money with just a fast scan from one of their fingers, as illustrated in Figure 1,
below. The finger vein system was implemented in order to diminish the
unauthorized Automated Teller Machines (ATM) withdrawals that had increased a
lot in the last years in Japan, due to ATM skimming devices that capture card data
and PIN’s in compromised ATM machines. Nowadays the Hitachi system already
crossed frontiers and it is being used in ATM in other countries, such as Poland and
Turkey [40].
Figure 1 - Finger vein system developed by Hitachi that is being used in Poland [8].
The Hitachi finger vein reader is also being used in other applications like door
openings or even to login into computers using a USB device, as shown in Figure 2.
Figure 2 - Hitachi USB finger reader [4].
By using a biometric trait like the finger vein pattern, the Hitachi system provides a
great help in the protection against fraudulent approaches to access the private
data of a legitimate user. The used trait has the advantage of being internal to the
human body and invisible except under very specific conditions. This system also
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requires the presence of live blood vessels, decreasing the remote chance of a
possible forgery.
The accuracy of a system used in a delicate area such as money withdrawal is a
very sensitive matter. The vein based system can meet all the accuracy
requirements because, with the right illumination conditions, there is an
extraordinary degree of variation between patterns which reinforces their
distinctiveness. In addition, the finger vein patterns do not change through all the
adult life time.
Another big advantage is the insensibility of this system to external factors like
dirt, sweat or grease of the finger. It is even possible to use this system using latex
gloves which increases the hygienic component of the system.
The Hitachi system works by illuminating the finger with near infrared light as can
be seen in Figure 3. The output of this system is an image with a distinctive pattern
that will be used to do the matching of the users.
Figure 3 - Finger Vein Capture Method [4].
Hitachi researchers found out that the best images are obtained by shining light
through the finger as illustrated in Figure 4. Hitachi developed a side illumination
system to address the problem of putting the finger inside the device. This
technique still uses the advantages of using transmitted light with the complement
of having an open and suitable device.
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Figure 4 - Illumination Technique [4].
To make the finger device usable for all kind of finger sizes and environments, the
light source intensity is adjusted automatically. This adjustment provides the
optimization of the image contrast, a higher image detail and the minimization of
the noise.
The authentication process of Hitachi was developed in four main steps: the
capture of the finger vein image, the normalization of the image, the feature
extraction and the matching, as depicted in Figure 5. In this case, the reference
template is stored in the smartcard to increase the level of protection.
Figure 5 - Block diagram of finger vein authentication [4].
Finger vein images are captured and moved into the CPU memory. After having the
images in memory, the algorithm dynamically adjusts the brightness of the
illumination source to improve the quality of the acquired image.
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In the second stage the finger vein image is normalized to accommodate all the
possible geometric changes in position or angle of the finger. The normalization is
achieved by identifying the outline of the finger in the acquired image, and then
rotating the whole image to normalize the slope of the outline.
The distinctive features of the finger blood vessel pattern, which will be used in the
matching stage, are extracted in the third stage. This step is crucial to eliminate the
variations provided by changes in the body metabolism or by the image conditions.
The result of the extraction step is a standard finger vein template of nearly 400
bytes which is appropriate to be used in the matching algorithm.
In the fourth and last stage the captured finger vein template is matched against
templates, stored in the database. If the matching score is below the predefined
threshold the user is successfully authenticated.
If the user is using a smartcard, the matching verification uses the template stored
inside the card. This provides extra security, since the reference template never
leaves the card, but on the other hand it reduces the security in the case of a
possible cloning of the card. Alternatively, the reference templates can be stored in
the finger vein device itself, on an attached PC, or somewhere else on the network.
All these storage approaches are vulnerable to a possible database breach.
1.1.2. Iris Recognition System
There are several companies that are developing and selling systems that rely on
the human iris to do people recognition. The iris is a thin circular diaphragm,
which lie between the cornea and the lens of the human eye. The iris is perforated
close to its center by a circular aperture known as pupil [41]. The human eye
description is depicted in the Figure 6.
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Figure 6 - Human eye [41].
The AOptix Technologies Insight Duo according to AOptix [10] is the first biometric
system that captures simultaneously iris and face images, adding a recognition
quality of a standard-based face record to the unparalleled uniqueness of iris
recognition.
The InSight Duo depicted in the Figure 7, was created in order to be used in all
kinds of environments, even with non-technical or non-acclimated users. This
system is currently used in the Gatwick airport in the south of London with the
goal of improving the overall airport experience.
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Figure 7 - AOptix Technologies InSight® Duo [9]
Another company, VisionBox, developed a hardware system called VB I-MATCH
[17], illustrated in Figure 8, that in addition to iris recognition also supports
fingerprints and facial recognition. This system is already being used at Schiphol
airport and also in land, air and sea borders in Portugal, UK, Finland and Norway
among others.
Figure 8 - VB I-MATCH developed by Visual Box. [17]
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1.2. Objectives of the Dissertation
This dissertation proposes to develop a low cost hand vein biometric recognition
system intended to do a highly efficient identification and to have a fast response
and easy usage. The application is native to Matlab and was compiled to be used in
a Windows operating system.
In this system the hand needs to be placed inside a special assembly, developed in
this thesis, which is shown in Figure 9. Inside the assembly the hand is exposed to
near infrared light, provided by high power leds. The assembly is essential to
control the illumination intensity, vital to acquire quality images.
Using the vein patterns as the biometric trait has a lot of advantages over the most
commonly used fingerprint and palmprint verification systems. Those advantages
are:
The veins are invisible except under special circumstances.
The system requires live blood vessels to work.
Despite being a low cost system the results obtained proved that the system is
reliable and it has a great potential. The target applications for this system are
countless. Opening doors, allowing ATM money withdrawals or unlocking a
computer are just some examples.
Figure 9 - Developed assembly.
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1.3. Contributions of the Dissertation
The main outcomes of this dissertation are:
An accurate low cost recognition system that uses the hand vein pattern as
the biometric trait.
An assembled prototype designed to acquire the hand vein pattern in
controlled conditions.
A Windows application with a user friendly interface.
A proof of concept based on results obtained with the system.
1.4. Structure of the Dissertation
This dissertation is organized in the following sections:
Chapter 2 - Biometric Systems: A brief review of the biometric
recognition systems available, the related problems, requirements and the
typical architecture.
Chapter 3 - State of the Art: A review of different techniques implemented
in the literature that might be used while implementing a biometric
recognition system.
Chapter 4 - Proposed Biometric Identification System: Description of
the architecture and implementation of the developed biometric
recognition system.
Chapter 5 - User Interface: In this section the user interface of the
developed system are revealed and explained.
Chapter 6 - Experimental Results: The experimental results obtained with
the developed system are presented and compared with the previous work
from the literature.
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Chapter 7 - Plans for the Future: Possible upgrades and improvements for
the developed system are proposed in this chapter.
Chapter 8 - Conclusions: Conclusions about the developed work are
drawn.
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2. Biometric Systems
Biometric systems are emerging as a new form of identification and access control,
already being used in a wide range of applications.
In this chapter the importance of biometric systems is analyzed, as well as their
requirements and challenges. The typical biometric system architecture is also
described, as well as the most commonly used performance evaluation techniques.
2.1. Biometrics Overview
Passwords and smart cards are nowadays the default ways of authentication, used
to grant access to protected information. Passwords are used commonly because
they are a simple and inexpensive mechanism to implement and use. However,
mostly due to weak characters combinations and poor password practices, they
are known for being a poor protection method [11]. They are also easy to steal and
forget. Biometric recognition systems surpass these problems because they only
depend on biological and behavioral characteristics, which are inherent to the
human individuals.
Any human characteristic can be used as a biometric characteristic as long as it
fulfills the following requirements [12]:
Universality - Every user should have the required trait.
Uniqueness - The trait used should be sufficiently different for each user.
Permanence - The trait used must be reasonably invariant over time.
Measurability - The trait must have an easy acquisition and measurement
and in addition, the obtained data must be easy to process.
Performance - The system must be accurate, fast and robust.
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Acceptability - The users must feel that the technology is useful and secure
in order to be compelled to have their biometric trait captured and
assessed.
There are a lot of human characteristics [12] that might be used in a biometric
system to identify an individual. These characteristics might be used on their own
(unimodal system) or in addition to each other to provide a stronger system
(multimodal system). Examples are:
Hand Palm Veins – Uses the vascular system patterns of the hand palm for
recognition.
Fingerprint – Uses the ridges and valleys pattern from the surface of the
fingertip.
Palmprint – Works in the same way as the fingerprints but uses a larger
area in the user’s palm.
Hand geometry – Works using a number of measurements taken from the
human hand, like its shape, size, length and widths of the fingers.
Iris Recognition – Uses the iris unique patterns to do the matching.
Face Characteristics – Utilizes the facial features for the recognition.
Gait – Exploits the movement characteristics of the user while walking.
DNA – Uses the diversity of the DNA characteristics.
Signature –Exploits the differences between users hand writing.
Voice – Uses the differences between the users acoustic spectrum of the
voice.
Depending on the goal for the given biometric recognition system, it may operate
in verification or identification mode.
Identification Mode – The system attempts to recognize an unknown
person by searching all the templates in the database for a possible match.
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Verification Mode – The system validates a user identity by comparing his
captured biometric data with a stored template. Instead of searching the
entire database for a template match, like in the identification mode, the
system directly accesses the template associated to a username or identity
card.
The biometric system needs to acquire data from the user. While capturing the
data, the interaction between the user and the system may create problems of
social acceptance or privacy issues to the users, as discussed in the next
subsection.
The typical biometric system architecture, some performance evaluation
methodologies and some hand recognition approaches are also discussed in the
following subsections.
2.1.1. Social Acceptance and Privacy Issues
The user willingness to use a biometric system is related to the interface easiness
and the comfort of the acquisition procedures. The systems that do not need
contact, like those using voice or iris images, are the most accepted because they
are more hygienic and user-friendly. On the other hand, systems that capture the
user characteristics without his perception are perceived as a threat to the privacy
by many users.
The privacy issue must be taken very seriously because the characteristics
obtained through biometric recognition systems may be used to provide additional
information about the individual. One good example of this problem is the retinal
pattern that may provide medical information about diseases of the user (e.g.
diabetes or high blood pressure). This is the kind of information that a health
insurance company could use in an unethical way to deny some benefits to an
individual that has a great risk of becoming sick [38].
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To prevent user’s fears of having the biometric identifiers compromised and used
in different systems and databases, the majority of the companies on the market
using this kind of technology do not store the physical characteristics in the
original form, but instead they store a template in an encrypted format, in order to
make the recreation of the original characteristics impossible.
2.1.2. Architecture of a Biometric Recognition System
Usually, a standard biometric recognition system is composed of six main modules,
according to the generic architecture illustrated in Figure 10:
Data Collection – To use a biometric system, it is essential to capture the
biometric data from a biometric sensor. In this step it is necessary to
correct errors, related to human factors, environmental conditions or even
due to the quality of the sensor used.
Preprocessing – This module is where tasks like image alignment,
enhancement or region of interest identification take place.
Feature extraction – The feature extraction module is where the region of
interest is extracted and converted on a suitable template.
Storage of the data in the database – In this module the biometric data is
stored in the database. A vector of numbers or an image with particular
properties is used to create the template, which is a synthesis of the
relevant characteristics extracted from the source. Elements of the
biometric measurement that are not used in the comparison algorithm are
discarded from the template in order to reduce the file size and to protect
the identity of the enrollee.
Matching – In this module the system checks the database for similar
templates. The matching is processed by computing a similarity score
between the new and the stored templates.
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Decision – The system verifies if the similarity score is greater than the
predefined threshold (t). If the score is lower than t, the template obtained
and the template stored are assumed to belong to the same individual.
Figure 10 - Biometric system main modules.
2.1.3. Performance Evaluation
Different measurements of the same individual, taken at different times will never
be exactly identical. To overcome this problem, a similarity score between the two
measurements is calculated. If that score is above a predefined threshold t it is
assumed that the two measurements do not belong to the same person. If the
score is below the threshold it is the other way around.
The False Accept Rate (FAR) and the False Reject Rate (FRR) can be used to
measure the accuracy of a biometric recognition system. FAR and FRR are both
functions of the system threshold t:
FAR - Is the probability of a successful access attempt by an impostor. The
impostor access happens if the similarity score between his template and a
genuine user’s template is less than the threshold.
FRR - Is the probability of a failed access attempt by a genuine user. An
incorrect reject happens when the score between the actual template and
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the template of the same user stored in the database is larger than the
threshold.
If the threshold value is decreased, making the system less tolerant to input
variations the FAR decreases, making the system more secure but, on other hand,
the FRR increases creating more difficulties to genuine users. The rate at which the
FRR equals the FAR is called the Equal Error Rate (EER). The FAR and FRR values
for different thresholds is depicted in the Figure 11.
The Failed to Enroll rate (FTE) is also an important measure to test the system
performance. It measures the percentage of unsuccessful attempts during the
creation of a template from a recently acquired image.
Figure 11 – Example of FAR and FRR for different threshold values.
The system’s response at all thresholds can be represented by a Receiver
Operating Characteristic (ROC) curve. That curve is a plot of the Genuine Accept
Rate (GAR) that is equal to (1-FRR) versus FAR for various threshold values. A ROC
curve is depicted at Figure 12. A perfect system would use an operating point
where the GAR would be 100% and the FAR 0%.
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
%
Theshold (%)
FAR
FRR
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Figure 12 – Example of ROC Curve
2.1.4. Comparison between biometric systems
Table 1, provided by the International Biometric Group [3], presents a comparison
among some of the most used commercial biometric systems, highlighting their
differences.
Table 1 - Comparisons between the FRR, FAR and FTE of different biometric devices [4].
Table 1 shows that FAR is typically in the order of 0.01% and the FRR is below 2%,
showing that the systems are very resistant to impostors, although that can create
some trouble to genuine users. Figure 13 shows the same information graphically.
0
20
40
60
80
100
0 20 40 60 80 100
GA
R(%
)
FAR(%)
ROC
ROC
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Figure 13 – FAR, FRR and FTE values for different biometric techniques [4].
The results presented by IBG [3] (see Table 1 and Figure 13) were obtained in
simulation scenarios, similar to real situations.
The best techniques, according to [3] are clearly the finger vein and the palm vein
due to the lowest FAR, FRR and FTE values. The finger vein got better FRR results
than the palm vein because it is easier to control the illumination on a smaller area.
The results presented above clearly show that the palm vein approach selected for
this thesis is a good choice.
2.2. Traits used in Hand Recognition Techniques
Palm vein patterns, palmprint or even the hand geometry, can be used to identify
an individual. If those characteristics are used wisely, they might be combined to
become the input of a multi-biometric technique and be used for identification
purposes. Those techniques are discussed with more detail in the following sub-
sections.
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2.2.1. Recognition Based on Hand Vein Patterns
A hand vein recognition system uses the vascular patterns of an individual as
personal identification data. The palm veins are convenient because they have a
complex vascular pattern and thus have a lot of unique features that can be used
for personal identification. Their falsification is also very difficult.
The hand vein detection process is based on a camera that takes a picture of the
subject’s veins under a source of infrared radiation at a specific wavelength. This
system exploits the vascular system to work. In the human physiology the
hemoglobin present in the blood is oxygenated at the lungs and then conducted to
the tissues of the body through the arteries. After the oxygen is released to the
tissues, the deoxidized hemoglobin returns back to the heart through the veins.
The rate of absorbency is different in the two types of hemoglobin. Deoxidized
hemoglobin absorbs light at a wavelength of about 760nm in the near infrared
region which is crucial for the system to work properly. The palm vein system is
able to detect veins but not arteries due to the specific absorption of infrared
radiation in blood vessels. We can use this technique to almost every part of the
body, however the hand is the most suitable body part because it is generally
available [2].
2.2.2. Recognition Based on Hand Palmprints
A hand palmprint recognition system uses the palmprint features to uniquely
identify an individual. Principal lines, wrinkles and ridges are shown in the Figure
14. The three principal lines of the palm are called the heart line, the head line and
the life line. These lines are unique and hard to miss and they almost don’t change
through the whole life of a person, which makes them a really good tool to build a
biometric identification system. The wrinkles are thinner than the principal lines
and are more irregular. Besides wrinkles and principal lines there are the ridges
that exist all over the palm.
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Figure 14 - The lines pattern of the palmprint. 1-heart lines, 2-head line, and 3-life line.[18]
Hand recognition can be based on the palmprint statistical features or on
palmprint structural features [18].
2.2.3. Recognition Based on Hand Geometry
Hand geometry recognition systems are based on measurements taken from the
human hand, including its shape, size of palm and length and widths of the fingers.
This technique has the advantage of being very simple and inexpensive.
Environmental factors such as dry weather or dry skin do not affect the
performance of the verification accuracy. This kind of system alone is not very
strong, but in addition to another system like the hand veins recognition system
can be very useful. This technique can work like a filter, or a soft-biometric,
excluding the users that have hands with a very different geometry from the one
recently acquired [19].
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3. State of the Art
The typical high-level architecture of most hand veins biometric systems is the one
depicted in Figure 15, below. It includes modules for image acquisition,
preprocessing, feature extraction and for the matching stage. Most differences
between biometric systems lay on the different approaches taken for each of the
blocks. In this chapter different techniques to process each of the different
modules are discussed.
Image Acquisition PreprocessingValid Image
Yes/No
Feature Extraction
Templates Database
User FoundYes/No
Matching
Access Granted
Access Denied
Figure 15 - Typical Recognition System Architecture
3.1. Image Acquisition
Infrared images of the veins can be obtained through the light reflection or
transmission methods.
In the reflection approach a light source and the camera are placed at the front of
the target, while in the transmission case it is located at the back of the target.
Figure 16 illustrates both cases.
Figure 16 – Image acquisition setup for the reflection approach (left) and transmission approach
(right)[1].
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According to Modris et al. [1], the transmission method allows obtaining better
results because it shows deep veins, while the reflection systems only shows
surface veins. One of the disadvantages of the transmission method, however, is
the necessity of a higher power light source, which may consume considerably
more power than the reflection method.
The system dimension is another disadvantage of the transmission technique. The
reflection method can be implemented in smaller spaces, because all the required
components can be attached on the camera side. Otherwise, the transmission
method needs to have the light source behind the palm which will increase the
total size of the system. Only the reflection method provides the capacity of
developing biometric systems for small devices, like mobile phones.
The amount of data that can be collected from a single image might also be a
problem of the transmission technique due to the acquisition of the bone structure,
which might hide the vein pattern that is used in the recognition process. The
captured images of the two techniques are depicted in the Figure 17.
Figure 17 - Infrared palm images captured by the reflection (left) and transmission (right) methods
[1].
Modris et al. [1], after taking into consideration the advantages and the drawbacks
of the transmission and reflection approaches, selected the reflection method over
the transmission method because of the power consumption and the size of the
system.
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The hardware setup proposed by Modris et al. [1] displayed in the Figure 18,
consists of a CCD camera, that needs to be sensitive in the near infrared spectrum,
an IR lens, an illumination system composed by IR LEDs, an IR band pass filter of
850nm wavelength and a palm fixing stand that is used to simplify the image
recognition task by avoiding the preprocessing tasks related to the rotation and
translation correction.
Figure 18 – Modris et al. experimental setup of palm vein infrared image acquisition [1].
After acquiring the images they are transferred to a PC to be saved on a database
and to be used in further processing. The image resolution used before selecting
the region of interest is 640 x 480 pixels.
According to Huan Zhang et al. [2] the hardware setup plays a very important part
in the design of a palm vein recognition system. The components of the image
acquisition module are a near-infrared camera and the illumination system. The
most important attribute of the camera used to acquire the images must be the
response to near infrared radiation. Attributes like spatial resolution and frame
rate are less important because the image must be still and the vein pattern details
are detected at short distance even with low resolution cameras.
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The arrangement of the illumination system in Huan et al. approach [2] has also a
great importance in the image acquisition process, since it will provide accurate
contrast between the veins and the surrounding tissue while keeping the
illumination errors at a minimum.
In order to avoid the influence of visible light that might compromise the quality of
the acquired image, the camera must have a low response in the wavelength of the
visible light. In [2], the authors use a JAI AD-080 CL 1/3’ CDD near-infrared
camera to avoid the described problem.
Due to the optical properties of the human skin, near-infrared light cannot
penetrate very deep in the human tissues, making the extraction of deep vein
patterns a very difficult task. The patterns used in [2] are mainly from superficial
veins. Due to the illumination setup used in [2] the statistical maximum distance of
penetration obtained is 3 mm, which will be a limitation of the quantity and quality
of the extracted blood vessels pattern.
The optical absorption and scattering coefficients must be taken into account while
doing the vein feature acquisition. The first one determines how far light can travel
under the human skin before losing its intensity. The second coefficient
determines how far light can travel before losing its original phase and had a
change in its direction.
The optical properties described above imply that the illumination conditions must
be homogeneous through the whole region of interest area and must be similar
through different acquisitions. The contrast must be high enough to provide a
reduction of the complexity of the preprocessing algorithms.
Huan Zhang et al. [2] developed a system that uses 850 nm near-infra LEDS as the
light source. To avoid the problems of the non-homogeneous illumination provided
by the LEDS, a holographic diffuser is used to provide constant illumination. The
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holographic diffuser scatters the light from the leds and diminishes the radiation
intensity.
In order to increase the uniformity of the acquired image, all the equipment is
inside a closed box (see Figure 19), painted with a black paint with high absorption
rate. The user hand is placed inside a small rectangular opening in front of the
camera.
Figure 19 - Huan Zhang et al. hardware setup [2].
3.2. Preprocessing
In order to use the captured palm images for a biometric application, the palm vein
pattern has to be extracted and segmented. This process is not trivial since the
blood vessels are almost undistinguished in some images. This section will review
some techniques that can be used in order to preprocess the acquired images
before the feature extraction module.
In Mauricio Ramalho’s proposed system [16] the preprocessing stage starts with
an image adjustment, where the image is converted to gray scale, resized and
filtered. The acquired image is resized to a maximum of 256x256 pixels in each
direction, in order to reduce the computational effort required. After resizing the
image, a low-pass Wiener filter is applied in order to eliminate the noisy areas,
smooth the textures and highlight the contrast.
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After the initial steps, the image is segmented into background and foreground.
Through a constant black background the hand region is acquired through an
automatic global histogram thresholding technique. This is accomplished by using
the Otsu’s thresholding method [43], which chooses the threshold value that
minimizes the intra-class variance of the output binary image.
The binary image obtained is the input of an algorithm based on morphological
reconstruction [21] that fills the holes, which might be present in the foreground
image area. After the reconstruction, the major object in the image (hand) is
selected and the hand contour is obtained.
After identifying the hand in the image, the region of interest (ROI) must also be
identified. The reference points that will be used in the ROI identification (Figure
20) will be acquired through two different techniques: The radial distance to a
fixed point [14] [15] and the contour curvegram [14].
Figure 20 – Obtained ROI area with Mauricio Ramalho approach [16].
After being detected, the ROI (area inside the blue square in the Figure 20) is then
normalized for comparison purposes. The ROI got to be normalized because
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different hands will have different ROI sizes and orientations. The normalization
consists on the rotation of the ROI to a vertical position followed by a resizing to
reduce the computational power required.
Huan Zhang et al. [2] image acquisition (see Figure 21) follows the same basic
preprocessing steps as Mauricio Ramalho [16], but instead of using a Wiener filter,
a Gaussian smoothing filter is used. Despite being different, the objective behind
the use of both filters is the same.
The ROI identification in Huan et al. approach is obtained through a technique
called inscribed circle-based segmentation. This technique calculates the circle that
meets the border of the palm in order to extract the larger area possible. One
advantage of this technique is that the dimension of the radius of the obtained
circle is different from person to person. That information can be used to conclude
almost instantly if two palms belong to the same person.
Figure 21 - Huan et al. acquired image with the inscribed circle [2].
The normalized ROI used in the feature extraction step is a predefined rectangular
area inside the obtained circle, see Figure 22.
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Figure 22 – Normalized ROI that will be used in the feature extraction module [2].
Michael Ong et al. approach [42] starts by identifying the ROI. The ROI localization
starts with the segmentation of the hand from the background through a skin-color
thresholding method. After the segmentation, a valley detection algorithm is used
to find the valleys of the fingers. Those points will be the reference points, used for
the ROI detection.
The enhancement of the contrast and the sharpness of the ROI images are obtained
through a Laplacian isotropic derivative operator, followed by the use of a
Gaussian low-pass filter used to smooth the palmprint images and bridge some
small gaps in the lines. Both the original and the preprocessed image are shown in
Figure 23.
Figure 23 – (a) The original palmprint, (b) palm print after the contrast adjustment and smoothing filters [42].
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3.3. Feature Extraction
In the feature extraction module the acquired biometric data is processed to
extract a set of discriminatory features for further recognition and matching. This
section will review some techniques that can be applied in order to extract the
necessary data from the acquired images.
There are three main categories of feature extraction techniques: (i) Appearance-
based (or Subspace-based), (ii) Texture-based and (iii) Line-based [27].
The appearance-based approach uses the palm print image as a whole. The most
used methods within this approach are the principal component analysis (PCA)
[34], linear discriminant analysis (LDA) [28] and independent component analysis
(ICA) [35].
The texture-based approach treats the palm print as a texture image. Therefore
statistical methods like Law’s convolutions masks, Gabor filters and Fourier
Transforms could be used to compute the texture energy of the palm print. Ordinal
measure [36] is another powerful method to extract the texture feature. It detects
elongated and line like image regions which are orthogonal in orientation. The
extracted feature is known as ordinal feature.
The line-based approach used by some researchers [29] uses the structural
information of the palm print. The features used are the line patterns, like principle
lines, wrinkles ridges and creases. Other researchers use more flexible approaches
to extract the palm lines by using edge detection methods like Sobel operator [30],
morphological operator [31], edge map [32] and modified radon transform [33].
Mauricio Ramalho [16] followed an appearance-based approach. The normalized
ROI obtained in the preprocessing stage, is converted into a binary vector of
luminance values, which is used as the input for PCA and LDA. This algorithm
linearly transforms the vector into a more discriminating feature space and
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reduces the data dimensionality. The set of linearly transformed features are the
templates that will be used in the matching stage.
Nuno Moço [23] used a texture-based approach. The feature extraction and
template creation modules are based on a technique called Orthogonal Line
Ordinal Features (OLOF), originally proposed in [5].
The idea behind this method is to qualitatively compare two elongated line like
image regions, which are orthogonal in orientation and generate one bit feature
code according to the observed differences. The set of code bits will be the
template used in the matching stage. The feature extraction steps using the OLOF
technique are present in Figure 24.
Figure 24 – Feature extraction using OLOF at [23].
Wu et al. [7] use a line-based approach which extracts the palm lines through an
edge detection method. The method used is the Canny edge operator [6] to detect
the palm lines.
Huan Zhang and Dewen [2] also used a line-based approach. After the detection of
the ROI, a Niblack [22] algorithm is used to convert to binary the acquired ROI.
Niblack’s algorithm is a local thresholding method based on the calculation of the
local mean and of the local standard deviation. The binary image is filtered by a
median filter to reduce the noise. The last steps are the region growth used to
remove the regions which are beyond the predefined vein width range and a
thinning method used to thin and repair the vein line. All the steps are depicted in
Figure 25.
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Figure 25 – Final preprocessing stages. (a) Target image. (b) Binarized image. (c) Filtered image. (d) Noise elimination. (e) Thinned image. (f) Repaired image [2].
3.4. Matching
The Matching is the last step in a biometric recognition system, where the recently
acquired template is compared with the ones stored in the database to provide a
matching score.
The matching score is a value that will quantify the similarity between the new
template and the templates stored in the database. The value of the matching score
indicates the chance of the two templates coming from the same individual. The
decision is based on a threshold. If the matching score is higher than the threshold,
the new template and the one stored in the database probably do not belong to the
same person.
Depending on the types of the features extracted, a variety of matching techniques
are used to compare two palm print images. In general these techniques can be
divided into two main categories, the Geometry-based matching and the Feature-
based matching.
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The Geometry-based matching techniques sought to compare the geometrical
primitives like points [24] and line features [25] on the palms. The point detector
method [26] uses a distance metric such as Hausdorff distance to calculate the
differences between two templates. On the other hand, the line based features
generally use Euclidean distances to compute the similarity between two line
segments. The Line Based matching is perceived as more informative than point-
based matching because the palm print pattern could be better characterized using
the rich line features, compared to isolated datum points [27].
The Feature-based matching works well for the appearance based and texture
based approaches. Researchers which studied the subspace methods like PCA,
LDA, and ICA use mostly Euclidean distances to compute the matching scores [28].
On a successful match it is highly expected that the value of Euclidean distance
should be zero or as low as possible. A smaller value of Euclidean distance
indicates a closest match and a larger value points to a very low probability of
finding a corresponding match.
The Feature-based matching has a great advantage over Geometry-based matching
when low-resolution images are used. This advantage comes from the fact that
Geometry-based matching usually requires higher resolution images to acquire
precise locations and orientations of the geometrical features [27].
When the palm print features are transformed into a binary bit string for
representation, the Hamming distance is utilized to count the bit differences
between two strings. The Hamming distance between two vectors is the number of
differences among the coefficients of the two vectors. If two vectors are equal, the
Hamming distance would be zero. The Hamming distance value is calculated with a
XOR operand between the two vectors [23]. The Hamming distance is obtained
through the equation (1).
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∑∑ ( ) ( )
( )
A successful matching is obtained if the Hamming distance result is below the pre-
defined threshold.
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4. Proposed Biometric Identification System
This section discusses possible applications for the developed system and
describes the technical options, the architecture and the procedures adopted for
the system development.
4.1. Potential Applications
The developed system is intended to work as an access control for certain areas or
applications. Despite being implemented with a low budget, it can achieve a good
performance. Some possible applications are:
Open Doors – Instead of using the old-fashioned key or even a magnetic card,
which might be lost, stolen or even replicated, the user just have to insert his hand
in the palm vein assembly in order to send a signal to an electronic locket. After
receiving the signal the electronic locket should open the door.
Online Bank Accounts Management – As an alternative of using regular
passwords and matrix cards, which are currently used in most bank corporations
to manage online accounts, the palm veins system could be used as a computer
peripheral to do a secure login.
ATM Machine Operations – This kind of functionality is already being used in
some countries, like Japan, where the system is attached in the assemblies of the
ATM stations to be used as supplement or substitute of the old PIN, which will
theoretically reduce the amount of unauthorized money withdrawals.
Access to High Secured Folders – Despite of being logged in your account, an
extra protection might be put in use to give access to highly valuable
documentation stored in the computer. That extra protection would be given
through the palm vein system that, once again, could be used as a peripheral.
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Unlock Computer – Instead of choosing the user name and password to do the
computer login, a brief hand palm vein scan should be sufficient to unlock the
computer.
4.2. System Architecture
The proposed biometric recognition system is unimodal and uses the hand vein
pattern as the biometric trait. The architecture of the developed system is
presented in Figure 26.
Pre-processingImage
Acquisition
Access Granted
Valid Yes/No
Feature Extraction
MatchingValid
Yes/No
Access DeniedBinary
Templates Database
Register
Register Yes/No
Figure 26 - Developed system architecture
The approaches taken for every module of the developed system will be explained
in detail in the following subsections. The following paragraph gives a summarized
description of the approaches taken.
To do the image acquisition in the developed system a modified low cost camera is
used. After the image acquisition, the captured image is resized in order to reduce
the required computational power, turning the preprocessing less demanding and
consequently saving processing time. After resizing the acquired image, it is
preprocessed in order to reduce the amount of noise. The detection of the region of
interest is obtained through some reference points in the hand contour. The
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feature extraction and template creation sections are based on the OLOF [5]
technique. As shown in [5], the OLOF method turns the veins representation
robust against illumination variations. It also makes the matching stage effortless
since the dissimilarities between two palmprints can be measured through the
differences in the binary bits from the two templates with a simple XOR operator,
which can be computed almost instantly.
4.2.1. Image Acquisition System
In this work the transmission illumination method was selected over the reflection
method because it shows deeper veins and allows the use of low quality cameras,
although this method requires higher energy consumption and more space.
The image acquisition module developed for this dissertation uses a low cost
webcam (Logitech QuickCam Pro 9000) that is installed in a special assembly (see
Figure 27), in order to operate in controlled illumination conditions.
Figure 27 - Developed palm vein pattern acquisition assembly.
Since the system performs recognition based on vein images, the illumination is
obtained using 15 near IR leds (OSRAM – SFH4550) [20], Figure 28.
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Figure 28 - System Illumination.
The box is a cube with 26 cm side. The top of the box was painted black in order to
reduce the interference caused by the visible light coming from the exterior of the
assembly.
In order to be able to capture the near infrared light, necessary for the vein
acquisition, the low cost web-camera requires the removal of the infrared filter
that is placed behind the lens, as illustrated in Figure 29. The main problem
associated with the removal of the IR filter is that the auto-focus functionality of
the web-camera becomes damaged, which turns the capture of good quality images
at long distances impossible. This problem will not affect the image acquisitions of
the developed system since they are captured from a small distance.
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Figure 29 – Square Infrared filter that needs to be removed in order for the webcam to capture infrared images.
As the camera needs to detect only infrared light, a visible filter has been applied.
An old fashioned photographic revealed film was used for this purpose, as shown
in Figure 30.
Figure 30 - Photographic film used to filter out visible light.
After removing the IR filter in the back of the lens and assembling the visible light
filter in front of it, the camera is ready to do the acquisition of near infrared
images, Figure 31.
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Figure 31 - Modified web camera used to do hand veins acquisition
With this assembly, images are acquired with a resolution of 240x320 pixels.
4.2.2. Preprocessing
The preprocessing stage prepares the image for the feature extraction phase. This
is obtained through several stages: image adjustment, filtering, segmentation,
contour detection, key point’s detection and region of interest extraction, as
represented in the architecture illustrated in Figure 32.
Image Adjustment
Image FilteringImage
SegmentationRaw Image
ROI Acquisition
Valid?Key Points Discovery
Contour Detection
Region of Interest
Extraction
No
Yes
Figure 32 - Developed preprocessing stages.
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The first step of the preprocessing stage is image adjustment. During this step the
raw image is resized from 240x320 to 192X256, in order to reduce the
computational power required through the process. After resizing the raw image,
the color space is converted from rgb to grayscale since the luminance information
is enough for the image segmentation, see Figure 33.
Figure 33 - Raw image and image after adjustment step.
The second step of the preprocessing chain is the filtering, used to reduce the noise
of the image and to smooth the areas with little variance. This is obtained using a
Wiener filter (the same type of filter used by Mauricio Ramalho in [16]). The
output of the Wiener filter is depicted in Figure 34.
Figure 34 - Image smoothed by a Wiener filter.
The third module performs image segmentation, where the image is segmented
into foreground and background through a pre-defined threshold. Thresholding is
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a very fast way of identifying the hand using the contrast with the black
background. After thresholding the image it is converted to binary. One example of
a segmented image obtained is depicted in Figure 35.
Figure 35 - Image segmented in foreground and background.
The segmented image is the input of the contour detection algorithm [13]. This
algorithm choses a random starting point in the hand boundary and then searches
for all the boundary pixels. The contour is essential for identifying the region of
interest and the reference points. The hand contour can be seen in the Figure 36.
Figure 36 - Hand contour.
The key point’s acquisitions are obtained through the combination of two different
techniques, the radial distance to a fixed point [14] [15] and the contour
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curvegram [14]. Both methods identify the fingertips and the valleys between the
fingers.
The radial distance to a fixed point technique calculates the Euclidean distance
between every contour pixel and a fixed point, which is the middle point of the
region where the wrist crosses the edge of the image.
The contour curvegram analyzes the intensity of the curvature along the contour,
and can be constructed by using a technique called difference-of-slopes [14].
The two methods have their benefits and drawbacks, but together they create a
stronger set of reference points. The radial distance to a fixed point is the first
technique used in order to get an approximation of the final reference points. After
obtaining the raw key points, the contour curvegram is used around the obtained
locations. The final obtained positions are the final fingertip and finger-valley
locations.
In order to obtain a good location of the fixed point, to be used in the radial
distance method, an ellipse (Figure 37) with the same normalized second central
moment as the hand region is drawn. Through the hand contour input, the ellipse’s
parameters like the major and minor axes, center position, end-points and lengths,
orientation (given by the angle between the major and minor axes) are calculated.
Figure 37 -Ellipse with the same normalized second central moment as the hand region.
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After obtaining the parameters that define the ellipse, it is necessary to find out in
which side of the minor axis the wrist is located. This verification is obtained
through the counting of the contour points that lie on each side of the axis. The
wrist is located on the side with fewer points. Knowing the axis’ side on which the
wrist lies, the fixed point (Figure 38) in the wrist is defined as the intersection
point between the major axis and the edge of the image.
Figure 38 - Fixed point marked as the half red cross.
The additional reference points, represented as yellow dots in Figure 39, are
necessary to extract the palm’s region of interest. These additional reference
points are determined by discovering, the thumb, index and pinkie fingers.
Figure 39 - Hand reference points.
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The final set of hand reference points, is composed by the five fingertips, the four
finger valley and the three additional reference points.
After finding the reference points, the square that represents the ROI is obtained
(Figure 40). The square position is defined through a line segment that is drawn
between the index and the pinkie finger.
Figure 40 - Region of interest acquisition.
Different hands will create squares with different sizes and orientations that will
need to be normalized for matching purposes. In order to do the standardization
the ROI is rotated to a vertical position and resized to a standard dimension. The
standard ROI dimension chosen is 128x128, due to the results that will be
presented in the performance evaluation section. Decreasing the dimensions
would reduce the computational effort but would also reduce the detail of the
image.
After the rotating step the image is converted to binary, filtered and then a
thinning method is applied in order to thin and repair the vein line. The ROI
treatment step can be seen in Figure 41.
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Figure 41 - ROI treatment steps.
After being thinned, the standardized ROI is converted into a vector consisting in
luminance values that will be used in the feature extraction module.
Through the reference points illustrated on Figure 42, the value of 35 hand
geometry characteristics will be calculated in order to provide the geometrical
information of the hand. The characteristics used are the finger widths (20),
perimeters (5) and lengths (10). After acquiring the 35 hand geometry features, a
mean of the 35 values is calculated. This mean summarizes the geometrical
information of the hand, so each user in the database will have one mean
associated. At the identification stage, the mean of the recently acquired template
under identification will be compared with the remaining geometrical information
(means) of the previously acquired data in the database. Instead of comparing
templates randomly, the most probable will be compared first.
The most probable users will be the ones that have similar hand geometry. If the
vein pattern under identification does not fit the one from the user with the most
similar geometry, the algorithm searches the next most similar and so on, until
finding the one with the same vein pattern. The delay obtained by calculating the
hand geometry characteristics is almost irrelevant, due to the simple calculations
required.
The geometry similarity is not crucial for a positive matching, as it was already
explained, but helps sorting the most probable hands.
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Figure 42 - Reference points used to calculate the hand geometry characteristics values.
4.2.3. Feature Extraction
The feature extraction module will output the biometric template, which will be
used in the matching stage. The feature extraction technique used in the developed
system is the Orthogonal Line Ordinal Features (OLOF) [5].
The vector of luminance values obtained in the preprocessing module will be the
input of the OLOF method that will generate a one bit feature code that is going to
be the template stored in the database.
The OLOF approach uses a 2D Gaussian filter to acquire the weighted average
intensity of a line-like region, equation (2)[5].
( ) ( (
( ) ( ) ( ) ( )
) (
( ) ( ) ( ) ( )
)
) (2)
In equation (2), symbolizes the orientation of the 2D Gaussian filter, the
filter’s horizontal scale and the filter’s vertical scale.
Equation (3) represents the orthogonal line ordinal filter, designed to compare two
orthogonal line-like palmprint image orientations for the same region [5]. The
image is turned into binary through the positive or negative output from the OLOF
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equation. If the difference between the two Gaussian filters is positive corresponds
to 1, if it’s negative corresponds to 0.
( ) ( ) (
) (3)
The filtering of the ROI is accomplished using three orthogonal line ordinal filters
through three different orientations (θ), in this case: OF (0), OF (
) and OF (
). The
filter parameters used were and . The filter is centered at( )
( ). The output of the feature extraction phase using the OLOF extraction
method are three bit ordinal codes based on the sign of the filtering results (Figure
43 and Figure 44).
Figure 43- OLOF output in the three directions,
and with scale ratio equal to 3.
Figure 44 - OLOF output in the three directions, θ=π/6, θ=π/3 and θ=0 with scale ratio equal to 2.
The scale ratio (
) is controlled to be equal or higher than 3 to make the shape line
like [5]. The scale ratio used is 3 due to the results obtained in the Figure 45 and
Figure 46. From those figures it is clear that if a Scale Ratio equal to 2 is used, a
really low FAR is obtained but in other hand very high FRR values are acquired, so
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it is not viable. The scale ratio equal to 4 in comparison with the scale ratio equal
to 3 obtains inferior FRR values but higher FAR results.
Figure 45 - FAR values for different scale ratios.
Figure 46 - FRR values for different scale ratios.
In order to reduce the computational complexity in the matching stage the three
resulting templates are resized. To find out which was the best template size, 300
different images with three different dimensions were used, 32x32, 64x64 and
128x128 for testing. Through the obtained results that are depicted in Figure 47, it
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
35 36 37 38 39
FAR
(%)
Threshold (%)
Scale Ratio 4
Scale Ratio 3
Scale Ratio 2
0
10
20
30
40
50
60
70
80
90
100
35 36 37 38 39
FRR
(%)
Threshold(%)
Scale Ratio 4
Scale Ratio 3
Scale Ratio 2
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became obvious that the template size does not affect significantly the accuracy of
the system. The OLOF template size chosen is then 32x32, to reduce the
computational complexity.
Figure 47 - FAR versus FRR for different template sizes.
After the resizing stage, the images are saved in the database as a binary file
template in order to be used in the verification stage. A small extract from the
database is depicted in the Figure 48, where the 5 first lines represent 5 templates
from the same user and the lines 6 and 7 represent a different user.
Figure 48 - Database example.
0.00
5.00
10.00
15.00
20.00
25.00
0.00 0.05 0.10 0.15 0.20 0.25
FRR
(%)
FAR(%)
32x32
64x64
128x128
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4.2.4. Matching System
A successful or unsuccessful recognition of an individual is based on the
calculation of the bitwise Hamming distances of the recently acquired template
and all the others in the database. The Hamming distance between two vectors is
the number of coefficients in which the corresponding symbols differ. If two
vectors are exactly equal, the Hamming distance will be zero. To calculate the
Hamming distance a bitwise XOR operator is used. The validation or refusal of the
matching is defined by a predefined threshold.
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5. User Interface
In this section the palm vein recognition system implementation and the
developed graphical user interface are detailed.
This system is native to Matlab and is compiled to work on a Windows operating
system without needing Matlab installation, just a small pack of public libraries
that come with the executable file.
When a user starts the program, the initial interface menu will pop up, as
illustrated in Figure 49. The user is presented with four options that are explained
in detail in this section:
Identify: For users already registered in the system that want to be
identified. This is the default option to be selected.
Enroll: The user wants to get registered into the system’s database.
Send Database: The user wants to send the database to the database
manager.
Database Control: The user wants to test and correct the database to
eliminate possible errors.
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Figure 49 - Initial Interface Menu
The first time the user runs the program, he must click in the “Enroll” button to
proceed with the registration, as shown in Figure 50.
Figure 50 - Enroll Interface
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After filling the personal identification the user should start to acquire his
biometric characteristic by presenting the left hand to the system and pressing the
“Register Left Hand” button. To do this, the user must place his left hand inside the
box with his palm covering the entire illumination circle (Figure 51). The hand
position inside the box is crucial to a good image acquisition. The palm must cover
the circle because it is the thicker part of the hand and so it needs more
illumination intensity. During the capture process the user should move his hand
slightly in order to capture images in slightly different positions/angles. A total of
five images for each hand will be acquired.
Figure 51 - Correct Hand Placement
While doing the capture the hand contour is displayed in the user interface. That
contour can be red or yellow, meaning bad hand positioning and good hand
positioning respectively.
When the contour turns green it means that the image capture is completed. An
example of all the possible cases is presented in Figure 52.
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Figure 52 –Three possible hand contours.
After finishing image acquisitions the algorithm will test them to conclude if they
were acquired correctly. If a problem occurs the system will prompt the user for a
new acquisition. Images may not be accepted due to different kinds of problems,
like noisy backgrounds and illumination disparities. An example of a corrupted
image is depicted in the Figure 53, where the bad hand placement led to a very
poor vein visibility.
Figure 53 - Example of a bad image acquisition.
To finish the enrollment of a hand, the system will ask the user to take the hand out
from the box and to insert it again in order to do a trial identification. If the user
name matches with the output displayed, the registration will be complete,
otherwise the system will ask for new images acquisition.
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After finishing the acquisitions of the left hand the user must follow the same
protocol for the right hand.
After completing the acquisition for both hands, a green tick will appear in the
enroll interface menu and the user will be registered in the database.
After the registration process the user can push the “Identify” button to proceed
with the identification. If a match is found the username and his Facebook
photograph (obtained through the Facebook username) are displayed in the
matching result interface, Figure 54.
Figure 54 - Matching Result Interface
The “Send Database” button in the initial interface Menu sends the entire local
database to the database manager email in order to increment his database with
the registries of each person registered on the current computer.
The “Database Control” button was created in order to test the database for
possible errors. It tests if the database has the expected size for the number of
registered users. If an error is detected, the user has the option of repairing the
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database, reenrolling all the users automatically. If the database is compromised,
the database control interface will display a red rectangle, Figure 55.
Figure 55 - Database Control Interface after detecting an error.
If there are no errors detected, a green rectangle is displayed, Figure 56.
Figure 56 - Database Control Interface after not detecting any error.
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6. Experimental Results
On this chapter the results of the tests performed with the proposed biometric
systems are discussed. The system performance was tested using the commonly
used biometric performance measures mentioned in section 2.1.3 as references.
6.1. Database Creation
In order to test the performance of the developed system, the first step was to
create a hand palm vein database containing 30 registered people. For each
person, five different acquisitions from each hand were performed. For testing
purposes each hand is considered as a different user. 30 registered people
represent a database of 300 different templates.
6.2. Performance Evaluation
The performance of the developed biometric system is evaluated by the ROC curve
which plots the FAR against the GAR (or 1-FRR). The performance is also evaluated
by the Equal Error Rate (EER), which is defined as the error rate when the FAR and
the FRR are equal. A recognition attempt might have the following results:
Type of user Match Non-Match
Genuine Correct Accept False Reject
Impostor False Accept Correct Reject
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The system performance was tested using the database mentioned above, using a
ROI size of 128x128 and the features were extracted with the OLOF technique. The
FAR, FRR and GAR results obtained with the developed system are presented at
Table 2.
Threshold (%)
FAR (%) FRR (%) GAR (%)
0 0,00 100,00 0,00
5 0,00 100,00 0,00
10 0,00 99,83 0,17
15 0,00 96,50 3,50
20 0,00 83,50 16,50
25 0,00 56,00 44,00
30 0,00 33,00 67,00
35 0,00 19,67 80,33
40 0,51 13,33 86,67
45 20,97 6,83 93,17
50 88,13 0,00 100,00
55 99,98 0,00 100,00
60 100,00 0,00 100,00
65 100,00 0,00 100,00
70 100,00 0,00 100,00
75 100,00 0,00 100,00
80 100,00 0,00 100,00
85 100,00 0,00 100,00
90 100,00 0,00 100,00
95 100,00 0,00 100,00
100 100,00 0,00 100,00
Table 2 - FAR, FRR and GAR for different threshold values.
The obtained ROC curve is shown in Figure 57. The ROC curve is near the perfect
point (0,100) which shows the good matching performance of the system.
The ROC curve and the table show that GAR is near 85% when the FAR is 0%.
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For applications like opening doors for not very high secure areas, an operation
point with values of FAR above 0% can be used despite the slope of the ROC curve
suggesting that the GAR increases very slowly in comparison with the FAR, so
there is no great benefit on using a GAR above 85%.
For ATM machine operations or Internet bank account managements the FAR must
be near 0%, which will result in an operating point leading to GAR values below or
equal to 85%.
Figure 57 - Receiver Operation Characteristic curve for a ROI with 128x128 pixels.
An alternative and simplified way of evaluating the performance of a biometric
system is through the EER. A low EER means that it is possible to get both low
values of FRR and of FAR and thus the lower the EER, the better the performance.
Despite being a good reference point, the EER might not be the ideal operating
point for a given system. The system might require a lower FRR or FAR for special
application conditions. A system that requires high security conditions, such as the
ATM machine, will require a really low FAR which will possibly imply a higher
FRR.
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
GA
R(%
)
FAR(%)
ROC
ROC
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Figure 58 shows the FAR and FRR curves produced in this dissertation as functions
of the threshold. The figure shows that when the threshold value increases, the
FRR decreases and the FAR increases. The figure also shows that if the threshold is
lower that 40% the FAR is near zero. Through the figure it is perceptive that the
EER of the developed system is near 9% and the associated threshold is about
45%.
Figure 58 - FAR and FRR at different operating thresholds.
In order to test which ROI size should be used, three ROC curves were created. The
three sizes tested were, 32x32, 64x64 and 128x128 pixels. The obtained result is
depicted in Figure 59.
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
%
Threshold (%)
FAR(%)
FRR(%)
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Figure 59 - ROC curve for different ROI dimensions.
From the Figure 59, it is obvious that the ROI size of 128x128 pixels and 64x64
obtain the best results in terms of matching. The ROI size of 32x32 pixels clearly
underperforms both in the ROC curve as well in the EER (see Figure 60).
Figure 60 - FRR (%) against FAR (%) to obtain EER for different ROI dimensions.
0
10
20
30
40
50
60
70
80
90
100
0 0.1 0.2 0.3 0.4 0.5
GA
R(%
)
FAR(%)
128x128
64x64
32x32
0
5
10
15
20
25
30
35
40
45
50
0 10 20 30 40 50
FRR
(%)
FAR(%)
128x128
64x64
32x32
EER
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The ROI size chosen for the developed system is 128x128 pixels due to the better
matching performance.
6.3. Operating Point Selection
The operating point used depends on the application. It must be chosen taking into
account the system recognition performance and the security of the system. The
developed system is intended to be used in several applications, as it is mentioned
in section 4.1. The operating points present on Table 3 were obtained with a
database of 30 different users, and might have slightly changes if the number of
users is increased.
Threshold (%)
FAR (%) FRR (%)
35 0,000 19,667 35,5 0,002 18,833 36 0,009 18,167
36,5 0,011 18,000 37 0,020 17,500
37,5 0,038 16,167 38 0,072 15,167
38,5 0,113 14,667 39 0,199 14,000
39,5 0,337 13,833 40 0,508 13,333
Table 3 - Values of FAR and FRR for different operating points.
If the system is intended to be used on an ATM machine or to do online bank
account operations, the operating point should be the one depicted in red. That
operating point implies that in every 1000 attempts to access the system 0
impostors (0.0%) will be accepted and around 197 genuine users (19,66%) will be
rejected which means that they require a new authentication attempt.
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If the purpose of the developed system is opening a garage door, the operating
point chosen should be the one depicted in green on the Table 3. Through this
operation point in every 1000 attempts to access the system, 5 impostors (0.5%)
will be accepted and around 133 genuine users (13.33%) will be rejected,
requiring a new authentication attempt.
If the application was intended to unlock a computer or access to some secure
folders, the operating point depicted in blue in the Table 3 could be used. This
operating point provides that in 1000 attempts 2 impostors (0.2%) will be
accepted and 140 genuine users (14%) will be rejected, requiring a new
recognition attempt.
It is important to highlight that the databases behind the results of each technique
discussed below are not the same, which limits their comparability. However,
these results are used as guiding points to evaluate the developed technique.
Mauricio Ramalho [16] in his palmprint recognition system used an operating
point that achieves 9.5% for the FRR, 0.1% for FAR and 3.29% for the EER. The
system proposed in this dissertation achieves slightly worst results, but has all the
advantages associated with the vein pattern over the palm print pattern
(invisibility expect under special conditions, being internal to the human body, and
providing liveliness prove). Also, Mauricio Ramalho [16] used an Olympus C-3020
Z digital camera that is clearly more expensive than the web-camera used in the
proposed thesis (Logitech QuickCam Pro 9000).
Nuno Moço [23] in his palmprint recognition system for cellphones used an
operating point that achieved 9.87% of FRR and 0.03% of FAR with an EER of
around 5%.
Huan Zhang and Dewen [2] on theirs hand vein recognition system achieved an
EER of 1.82% with an AD-080CL camera that costs around 3000 €, which is 120
times more expensive than the web-camera of the proposed system (25 €).
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7. Plans for the Future
During the progress of this thesis an alternative system was also created. It had the
purpose of using the hand texture pattern as the identification factor, in non-
controlled environments. Due to time constraints it was not possible to turn this
system robust enough.
The hand texture pattern recognition system was intended to be used to:
Unlock Computer – Instead of choosing the user name and password to do the
computer login a brief hand palm scan should be sufficient to associate the data
obtain to a specific user.
Unlock Cellphone – In a similar way as the approach taken before, a cell phone
version might be developed in order to do a cell phone unlock.
This system uses a similar algorithm as the vein pattern recognition system with
some improvements. The main difference is the usage of a background subtraction
algorithm, which is used to simplify the hand detection phase. The background
subtraction algorithm turns all the pixels inside the areas without movement black.
The hand detection phase is accomplished with the help of a skin detection
algorithm that searches the images for skin color pixels. The hand detection phase
can be seen in Figure 61.
Figure 61 - Hand texture recognition system using a regular laptop computer camera.
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The main objective in the near future is turning the hand feature recognition
system more robust and combining it with the hand vein recognition system on a
special assembly.
The developed hand veins recognition system also requires some development in
order to become even more robust:
The developed box should be painted in black in order to reduce the
amount of visible light present inside the box during the acquisitions.
It should be possible to automatically regulate the IR illumination intensity
inside the box in order to obtain good results for hands with all sizes and
thicknesses.
The system should be bimodal and require a hand geometry matching for a
valid authentication.
The database size should be larger in order to test the system performance
more accurately.
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8. Conclusions
This dissertation presents a unimodal biometric recognition system that used the
hand vein patterns to do the identification of an individual. It was developed in
Matlab and implemented to work on a Windows operation system.
The developed system has proved to work well for the application scenarios
considered, such as ATM operations, opening doors and unlocking computers. In
addition, it has the advantage of being low-cost, requiring an investment of around
50€ and of being simple to assemble in comparison with the existing recognition
systems, as discussed throughout this dissertation.
Through testing, several operating points were obtained and associated to
different applications, with the objective of obtaining the best response from the
system in terms of recognition and security performances for the different
purposes of use. The EER of the developed system is near 9%. The ROI dimension
used is 128x128 pixels due to the best matching results during the tests. The OLOF
templates dimensions used in order to provide a smaller database without losing
performance is 32x32.
The OLOF approach used has proved to be very effective at providing good
biometric recognitions results for the tested database and in the live acquisitions
as discussed in chapter 4.2.3.
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9. References
[1] Greitans, M., Pudzs, M. & Fuksis, R., 2010, “Palm Vein Biometrics Based on
Infrared Imaging and Complex Matched Filtering”, Proceedings of the 12th
ACM Workshop on Multimedia and Security, pp.101-106, New York, USA.
[2] Zhang, H. & Hu, D., 2010, “A Palm Vein Recognition System”, IEEE
Proceedings of the International Conference on Intelligent Computation
Technology and Automation, pp.285-288.
[3] The International Biometric Group – available at:
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[4] Edgington, B., 2007 “Introducing Hitachi’s Finger Vein Technology-A
White Paper”, Hitachi’s Finger Vein Technology, Version 1.0.
[5] Sun, Z., Tan, T., Wang, Y., 2005, “Ordinal Palmprint Representation for
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[6] Canny, J., 1986, “A computational approach to edge detection”, IEEE
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[8] Palm Print ATM at Poland - available at:
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[9] AOptix InSight Duo used at Airports - available at:
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[10] AOptix InSight Duo technology – available at:
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[11] Passwords Weaknesses – available at:
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[12] Jain, A., Ross, A. & Prabhakar, S., 2004 “An Introduction to Biometric
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[13] Shapiro, L. & Stockman, G., 2000, “Computer Vision”, Prentice Hall, Upper
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[14] Konukoglum, E., Yorukm, E., Darbon, J. & Sankurm, B., 2006, “Shape-Based
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[15] Lin, C., Chuang, T. & Fan, K., 2005, “Palmprint Verification using
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[16] Ramalho, M., 2010, “Secure Palmprint Verification System”, Master Degree
Dissertation, Instituto Superior Técnico, Lisboa.
[17] Vision-box developed system – available at: http://www.vision-
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[18] Connie, T., Jin, A., Ong, M. & Ling, D., 2005, “An Automated Palmprint
Recognition System” , Image and Vision Computing, 15 (5), pp.501-515.
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