webcam based fingerprint authentication for personal identification system

COLLEGE SCIENCE IN INDIA

Pay Attention, Gain Understanding Vol. 1 : 3 December 2007

Board of Editors

S. Andrews, M.Sc., M.Phil., Editor-in-Chief

S. Lalitha, Ph.D.

Poornavalli Mathiaparnam, M.A., M.Phil.

M. S. Thirumalai, Ph.D., Managing Editor

Webcam Based Fingerprint

Authentication for Personal Identification

System

Md. Rajibul Islam, Md. Shohel Sayeed, and Andrews Samraj

College Science in India 1 : 3 December 2007 Webcam Fingerprint Personal Identification Islam, Sayeed & Samraj 1

Webcam Based Fingerprint Authentication for

Personal Identification System

Md. Rajibul Islam, Md. Shohel Sayeed, Andrews Samraj

Abstract: In the networked world there are a huge

number of systems that need biometric recognition, so at

present it has become an important issue. For the personal

identification various kinds of vision-based techniques have

been proposed earlier. We present a novel one based on

visual capturing of fingerprints using a webcam.

Fingerprint image quality influences deeply the

performance of fingerprint identification systems. This

paper presents an improved authentication system using a

low priced webcam as well as a preprocessing approach

using gamma manipulation and gamma correction

technique to adjust lightness and intensities of the

fingerprint image due to enhance fingerprint image quality.

We also implement and test our proposed approach using

the FVC2004 database including the webcam database of

1200 fingerprint images which is obtained by proposed

approach and compare the EER (Equal Error Rate), FRR

(False Rejection Rate) and FAR (False Acceptance Rate) of

each database. Experiment- al results show that our

approach performs significantly improved and

comparatively EER, FRR, FAR of the webcam database

are very similar to the FVC2004 database.

Index Terms- fingerprint authentication, webcam,

fingerprint, fingerprint sensor, gamma manipulation,

gamma correction.

I. INTRODUCTION AND MOTIVATION

Biometrics proposes an effectual approach to identify subjects

because it is concerned with the unique, reliable and stable

personal physiological features. These features can be: iris [8],

[9], fingerprints [6], [7], palmprints [10], [11], hand geometry

[12], [13], faces [14], [15], voice [16], [17], etc. Most of them

are used for Vision based identification. Voice recognition or

signature verification are the most widely known among the

non-vision based ones. Among these, fingerprint identification

has been the most widely browbeaten because of stability,

usability, and low cost. A fingerprint sensor is necessary for

the commercial fingerprint identification system. Unfortunately

almost all the modern sensor products are not so cheap and

available in the market. Therefore we’ve used a low priced

webcam to fabricate our authentication system. But there are

challenging problems when developing fingerprint

identification system using a webcam. First, the contrast

between the ridges and the valleys in images obtained with a

webcam is low. Second, because of the finger is not flat and

the image captured by webcam is low resolution image, some

parts of the fingerprint regions are clear but some parts are

blurred, even it is impossible to extract ridges and valleys.

Third, the lightness of captured fingerprint image is so bright

and blur. The overall fingerprint identification system using a

webcam is composed of preprocessing using gamma

manipulation and gamma correction, image enhancement,

feature extraction, and matching algorithm. Fig. 3 shows the

block diagram of the overall system.

This paper presents an overview of the whole interface and a

novel approach to capture fingerprint images using webcam

and preprocessing these images in order to improve the

enhancement and extraction system. We have divided this

paper in the following way: In the next part of this section,

briefly presents an overview of some fingerprint scanner. In

section 2, we describe about the webcam dataset, data

collection, overview of the whole system, our contribution

especially gamma manipulation and gamma correction

technique of the preprocessing stage before image

enhancement and feature extraction in our authentication

system. After that, in section 3, we describe the experiments,

discussions using the data obtained from the proposed

approach and the data from FVC2004 and a comparative result

also presented. Possible future work perspectives is described

in section 4 and by the end of this paper, we present

conclusion.

A. Overview of some Fingerprint Sensors

We may not realize it, but the ridges in our fingertips have

evolved over the years to allow us to grasp and grip objects

with our hands. The ridges and valleys of skin are formed

based on genetic and environmental factors, thus, fingerprints

are said to be unique from individual to individual. Even

identical twins do not share the same fingerprints.

A fingerprint sensor is an electronic device used to capture a

digital image of the fingerprint pattern. The captured image is

called a live scan. This live scan is digitally processed to create

a biometric template (a collection of extracted features) which

is stored and used for matching. Following are the overview of

some of the more commonly used fingerprint sensor

technologies [18].

There are two basic methods for scanning fingerprints: Optical

scanning and capacitance scanning. Besides Ultrasonic sensors

also have been used to scan fingerprint.


Optical Optical fingerprint imaging involves capturing a digital image

of the print using visible light. This type of sensor is, in

essence, a specialized digital camera. The top layer of the

sensor, where the finger is placed, is known as the touch

surface. Besides this layer is a light-emitting phosphor layer

which illuminates the surface of the finger. The light reflected

from the finger passes through the phosphor layer to an array

of solid state pixels (a charge coupled device) which captures a

visual image of the fingerprint. A scratched or dirty touch

surface can cause a bad image of the fingerprint. A

disadvantage of this type of sensor is the fact that the imaging

capabilities are affected by the quality of skin on the finger.

For instance, a dirty or marked finger is difficult to image

properly. Also, it is possible for an individual to erode the outer

layer of skin on the fingertips to the point where the fingerprint

is no longer visible. It can also be easily fooled by an image of

a fingerprint if not coupled with a "live finger" detector.

However, unlike capacitive sensors, this sensor technology is

not susceptible to electrostatic discharge damage.

Capacitance

Like optical scanners, capacitive fingerprint scanners generate

an image of the ridges and valleys that make up a fingerprint.

But instead of sensing the print using light, the capacitors use

electrical current.Capacitance sensors utilize the principles

associated with capacitance in order to form fingerprint

images. The two equations used in this type of imaging are:

V

QC = ………(1)

d

AC ro∈=∈ ………..(2)

Where

C is the capacitance in farads

Q is the charge in coulombs

V is the potential in volts

�0 is the permittivity of free space, measured in farad per meter

�r is the dielectric constant of the insulator used

A is the area of each plane electrode, measured in square

meters

d is the separation between the electrodes, measured in meters.

In this method of imaging, the sensor array pixels each act as

one plate of a parallel-plate capacitor, the dermal layer (which

is electrically conductive) acts as the other plate, and the non-

conductive epidermal layer acts as a dielectric.

Passive capacitance A passive capacitance sensor uses the principle outlined above

to form an image of the fingerprint patterns on the dermal layer

of skin. Each sensor pixel is used to measure the capacitance at

that point of the array. The capacitance varies between the

ridges and valleys of the fingerprint due to the fact that the

volume between the dermal layer and sensing element in

valleys contains an air gap. The dielectric constant of the

epidermis and the area of the sensing element are known

values. The measured capacitance values are then used to

distinguish between fingerprint ridges and valleys.

Active capacitance

Active capacitance sensors use a charging cycle to apply a

voltage to the skin before measurement takes place. The

application of voltage charges the effective capacitor. The

electric field between the finger and sensor follows the pattern

of the ridges in the dermal skin layer. On the discharge cycle,

the voltage across the dermal layer and sensing element is

compared against a reference voltage in order to calculate the

capacitance. The distance values are then calculated

mathematically, using the above equations, and used to form an

image of the fingerprint. Active capacitance sensors measure

the ridge patterns of the dermal layer like the ultrasonic

method. Again, this eliminates the need for clean, undamaged

epidermal skin and a clean sensing surface.

Ultrasonic

Ultrasonic sensors make use of the principles of medical

ultrasonography in order to create visual images of the

fingerprint. Unlike optical imaging, ultrasonic sensors use very

high frequency sound waves to penetrate the epidermal layer of

skin. The sound waves are generated using piezoelectric

transducers and reflected energy is also measured using

piezoelectric materials. Since the dermal skin layer exhibits the

same characteristic pattern of the fingerprint, the reflected

wave measurements can be used to form an image of the

fingerprint. This eliminates the need for clean, undamaged

epidermal skin and a clean sensing surface.

Webcam Webcams typically include a lens, an image sensor, and some

support electronics [19]. Various lenses are available, the most

common being a plastic lens that can be screwed in and out to

set the camera's focus. Image sensors can be CMOS or CCD,

the former being dominant for low-cost cameras, but CCD

cameras do not necessarily outperform CMOS-based cameras

in the low cost price range. Consumer webcams usually offer a

resolution in the VGA region, at a rate of around 25 frames per

second. The higher resolution of 1.3 Megapixel is also

available in the market.

The camera pictured to the right, for example, uses a Sonix

SN9C101 to transmit its image over USB. Some cameras -

such as mobile phone cameras - use a CMOS sensor with

supporting electronics 'on die', i.e. the sensor built on a single

silicon chip, to save space and manufacturing costs.


II. THE WEBCAM DATASET

The dataset consists of 1200 fingerprints of 150 fingers with 8

impressions per finger. Fig. 1 shows three typical images from

the data. The task is not trivial:

• The fingerprints of 150 people were captured on multiple

days during a one month period. We captured 8 impressions

for each finger by a snap shot for each single impression.

• The webcam is a low quality webcam. Each snap shot has

640 × 480 resolution so ridges and valleys of fingerprints

are not so clear. Lighting on the fingerprint is also an

enormous problem against capturing good quality fingerprint

with clear ridges and valleys.

• Basically in the beginning we noticed that only a small part

of fingerprint got the clear features of ridges and valleys

where the rest of the part didn’t get. Because the finger is not

flat, that’s why we used a piece of transparent glass in front

of the webcam and a light source in between webcam and

transparent glass. A person could turn their fingerprint away

from the webcam, and roughly one third of the images

contain half fingerprint at all. Since only a few fingerprints

are labeled, and all of the test images are available, the task

is a natural candidate for the application of semi-supervised

learning techniques.

(a)

(b)

(c)

Fig. 1: fingerprint images captured by webcam are presented from our webcam

dataset.

A. Data Collection

We asked all of our friends and their friends as our volunteers

to provide their fingerprint in the webcam of our fingerprint

authentication system which takes over one month. Not all

participants could provide their precise fingerprint for every

take. The webcam is located in the Image Processing and

Telemedicine Laboratory with a Pentium 4 PC, and took

fingerprint images from the webcam whenever a new frame

was available. In each take, the participants pressed their

thumbs on the transparent piece of glass. And like this they put

eight times and after capture their fingerprint they removed

their thumbs from the glass. It took five to ten seconds for per

impression capture. As a result, we collected fingerprint

images where the individuals have varying fingerprints for

eight impressions from the same finger in different rotation

angle from the webcam. We discarded all fingerprints that were

corrupted by hasty movement.

B. Overview of our Proposed System

Fig. 2 presents a process we’ve used to capture fingerprint and

Fig. 3 shows our proposed block diagram of the whole

authentication system and the experimental setup. For this

proposed system we have used webcam dataset which is

described in section 2.0 and section 2.1 and to evaluate the

performance of matching we have used FVC2004 [21]

datasets. The preprocessing stage performs the initialization of

the algorithm, i.e. it captures a colorful low resolution

fingerprint image (shown in Fig. 1) and convert it to grayscale

image and performs the gamma manipulation and gamma

correction to adjust lightness and intensities of the fingerprint

image. And then sends it to the fingerprint enhancement block.

The fingerprint enhancement block has the task of enhance the

fingerprint on each impression of each user by using the code

loosely follows the approach presented by P.D. Kovesi [1]. Just

before feature extraction a thinning process needs to be

performed as indicated in [2]. In which two tests are run one

after the other until none of them discover pixels that need to


be removed. However, this method did not meet the

requirements imposed to a thinning algorithm because it still

left some spurious structures that did not permit a single point

inside a line to have only two neighbors, a ridge-end only one,

and a bifurcation three. The conformance to the established

criteria was obtained by the creation of a third test to be run

once after the former two are passed to test for certain

conditions in matrices of 3x3 pixels that indicate a spurious

structure that shall be properly modified.

The minutiae extraction process, defined in [3], uses matrices

of 3x3 pixels to search for typical minutiae, that is: ridge

endings and ridge bifurcations. After extraction minutiae the

extracted data stores to the system database. Finally for the

matching process, the live extracted data is to be compared

with the extracted data stored in the system database.

Fig. 2: 3D model of the fingerprint capturing by webcam. Finger to press on

the transparent piece of glass. Lighting should be 45o angel in respect of the

glass and webcam should be in a considerable distance.

(a) Enrolment Process

(b) Verification Process

Fig. 3: (a) Block Diagram of the Enrolment system. The webcam captures a

fingerprint impression to the preprocessing stage. This block initializes the algorithm and selects the grey scale fingerprint to perform the enhancement.

The enhanced fingerprint obtains the position of the minutiae in the feature

extraction block and finally stored the extracted data in system database

block. (b) Block Diagram of the verification system. Like the enrolment

system after successfully satisfied all the blocks until Feature extraction, live

feature data will verify with the feature data stored with the system database.

C. Our Contribution in this Paper

The system we proposed is the most remarkable and automatic

security device. For this system we connect a webcam of low

priced and low resolution as well, to the computer and apply an

identification system to examine the fingerprint it sees, and

then compare this fingerprint image against the minutiae of the

fingerprint belonging to the endorsed user to perform the

matching process. We attempted both, a relatively high

end Creative unit and a low end Logitech QuickCam, while the

proposed protocol ready to capture fingerprint images by such

USB-connected webcam and perceived no dissimilarity in

performance. It gives the impression that a particularly high

resolution fingerprint image does not influence the fingerprint

detection process on the way to identify the necessary features

which we have illustrated below in this section. Alternatively,

in accordance with the changing of lighting circumstances we

detected a difference. Our proposed scheme has more

complexity classifying the features of the fingerprint when the

light source is at the back of the finger, than when the lighting

is at the front or to the side. One more difficulty is to detect

the minutiae information accurately from such low resolution

and poor quality fingerprint image which is captured by low

priced webcam (see Fig. 4).

(a)

(b)

Fig. 4: (a) Fingerprint image capture by webcam without using crystal clear glass. (b) Fingerprint image after enhancement.

To conquer this problem we have used a piece of crystal clear

glass. The fingerprint will be captured from the reverse side

once the user pushes his finger on the glass. And between the

webcam and glass we have used light source, lighting to the

side because if the lighting is facing the glass it reflects and

Yes/ No

Glass

Webcam

Preprocessing

stage

Fingerprint

enhancement

Feature

extraction

System

Database

Matching


captured a shadow of light with the fingerprint image. It

doesn’t provide better result to the ridges detection of

fingerprint image, due to the lightness of the fingerprint images

captured by low resolution webcam which is so bright and blur.

Therefore we have used a preprocessing stage in our system to

perform gamma manipulation and gamma correction to bend

lightness and intensities of the fingerprint image. The results

are exposed in Fig. 7 and Fig. 8.

D. Gamma Manipulation in Fingerprint image

The fingerprint images are not gamma corrected which are

captured by webcam. In the preprocessing stage when the

image processing operations are performed on color fingerprint

images, it is normal that the production of out-of-gamut pixels

is not prevented. The gamut mapping may reduce the effect of

the image processing algorithm [5]. We offer a standard

method that allows lightness processing on grey image without

exceeding the limits of the gamut of the technique.

It will be asked to demonstrate gray scale images in Matlab in

such implement. Therefore if P is an image that takes on the

values [0,1,……..,255], then it may be presented by using the

following commands.

image(P+1);

axis('image');

graymap = [0:255; 0:255; 0:255]'/255;

colormap(graymap);

The lightness processing is a function of the color of the pixel

in gamma manipulation to change the desired lightness and a

maximum and minimum lightness per pixel. This maximum

and minimum depend on the position of the pixel in the gamut

of the fingerprint image and the relation between the lightness

change and the chroma change. The hue of all pixels is kept

constant.

A selection of grey value algorithms can be applied on color

fingerprint images using the proposed method. We show the

results for contrast enhancement in this part, by gamma

manipulation. A gamma manipulation’s outcome is that the

lightness values are distributed nonlinearly over the range that

is used. At the cost of decreasing the contrast in other regions,

this may increase the contrast in one or more regions of the

lightness range. The universal form of gamma manipulation is

shown in Fig. 5, mathematically described by:

γ

��

��

�

−

−−+=

*

min

*

max

*

min

**

min

*

max

*

min

* *)(LL

LLLLLLout

(3)

L* and *

outL are the input and output lightness and *

minL and

*

maxL are the minimum and maximum of the lightness range.

When this manipulation is used with γ >1, the higher (lighter)

lightness range gains more contrast, at the expense of the

contrast of the darker colors. At the same time the mean

lightness is decreased, i.e. all new colors are darker than the

original colors. When γ < 1, the opposite occurs (more

contrast in the darker colors, less contrast in the lighter colors,

and mean lightness increases).

Fig. 5: Gamma manipulation in fingerprint image. The result of equation 3 is

given for three different gamma values. It can be seen that for � < 1 the

lightness of the image (*

outL ) is always higher than for the original image

(*

inL ), and that darker colors have more contrast. For � > 1 the opposite

holds true.

Relative lightness change mapping This method is parameterized to allow various lightness change

levels and uses the perceived attributes of lightness and chroma

rather that a spherical coordinate system which must be

different for each setting of the direction parameter [20].

Assume that there is no longer any convergence point. The

algorithm is straightforward without the need for any iteration.

If ),(ˆ ***hLCC outλ< or ),(ˆ),(ˆ ****

hLChLC outin < then

do nothing, else

),(ˆ

)),(ˆ)()((

100 **

******

*mod

*

hLCC

hLCCLhLLL

outref

outcusp

λ

λα

−

−−+= (4)

),(ˆ),(ˆ

),(ˆ),(ˆ)1(),(ˆ

**

mod

**

**

mod

*

**

mod

**

mod

*

modhLChLC

hLCChLChLCC

outin

out

outoutλ

λλλ

−

−−+=

(5)

where ),(ˆ ** hLCin and ),(ˆ ** hLCout are the boundaries of

the image and the reproduction gamuts, respectively;

)( ** hLcusp is the lightness of the cusp at a given hue angle.

refC is a parameter that influences the curvature of the

mapping direction and must be greater than the largest possible

chroma, e.g. 2128 for TIFF-CIELAB images; λ is the

*

inL

*

outL

� = 0.5

� = 1.0

� = 2.0


degree of soft clipping and α gives the degree of lightness

change (0-100%). In the constant hue plane the original color point can travel

over the path. This path has the property that chroma (*C ) is

constant. In this section we discuss possible steps for gamut-

limited manipulations.

(a) *C = constant, the lightness is manipulated while keeping

the chroma *

C constant. The maximum and minimum values

are the maximum and minimum lightness *L for this particular

*C value.

(b) ** / LC = constant, the lightness is manipulated while

keeping the ratio **

/ LC constant. The minimum value is per

definition 0, the maximum value is the lightness value for

which the line ** / LC intersects the gamut boundary.

(c) Mapping towards black and white, the lightness is

manipulated in such a way that the point in the

chroma/lightness space moves towards black for a lightness

decrease and towards white for a lightness increase.

(d) Mapping away from black and white, the lightness is

manipulated in such a way that the point in the

chroma/lightness space moves away from black for a lightness

increase and away from white for a lightness decrease. The

maximum and minimum lightness are given by the

intersections of both lines with the gamut boundary.

Paths along which a color point may move within the constant

hue plane, when applying different steps for gamma

manipulations. For each step for an original point, the range for

lightness changes is applied, along with the result for � = 2 and

� = 0.5 and we obtained good outcome for �=0.9.

E. Gamma Correction in Fingerprint image

Gamma correction is used to reimburse the nonlinear behavior

of a poor and low resolution fingerprint image. Using a high

quality Digital camera most often images are already encoded

in gamma corrected, and will appear excellent when displayed

on most video monitors but for the fingerprint image captured

by webcam has to encode in gamma corrected form before

using the enhancement algorithm to obtain better result and to

improve the feature extraction system. However, if a

fingerprint image is stored with a linear scaling it becomes

necessary to correct the image. If the value of gamma for the

webcam is known, then the correction process consists of

applying the inverse of equation (6). γ

��

��

�=

255255

qp (6)

Where q is the original pixel value and p is the pixel intensity

as it appears on the display. This relationship is illustrated in

Fig. 6.

The fingerprint images captured by webcam, especially during

the gamma manipulation they are not corrected for the

nonlinear relationship between pixel value and displayed

intensity that is typical for a webcam. This nonlinear

relationship is roughly a power function, i.e.

gammavaluepixelIntensityDisplayed ∧= __ .

Fig. 6: approximate curve to show the intensity response over pixel value

This is an approximated curve to show how the intensity

response of a fingerprint image captured by webcam is

nonlinear. Bright colours tend to be displayed too bright. This

can be corrected. The process of adjusting the intensities to

look correct is known as Gamma Correction.

The amount of Gamma Correction we shall call G is usually

greater than 1. The range of displayable intensities, P, is

between 0 and 1. The formula is thus:

)/1( GPpixel ∧= (7)

A G value of 1 gives no Gamma Correction. Higher values

give more correction.

Because values of P must be between 0 and 1, it will have to

divide the intensity by the maximum displayable intensity,

perform the Gamma Correction, and then multiply up again.

tyMaxIntensiGtyMaxIntensiPpixel ∗∧= ))/1()/(( (8)

(a) (b)


(c) (d)

Fig. 7: (a) Fingerprint image captured by webcam, (b) Grayscale conversion,

(c) Fingerprint image after gamma manipulation and inverting the gamma

correction, (d) Enhance part of fingerprint-c.

(a) (b)

Fig. 8: (a) Image enhancement before preprocessing, (b) Image enhancement

after preprocessing

III. EXPERIMENTS AND DISCUSSIONS

We accomplish experiments with data of fingerprint

verification competitions, to demonstrate the advantages of our

proposed approach to fingerprint verification using low-priced

webcam.

A. Datasets

We applied an enhanced fingerprint matching approach using

TSVM [4]. In order to prove the influence of different image

qualities and image amount to our proposed approach we have

collected 5 datasets and out of these 5 datasets, four from

FVC2004 (The Second International Fingerprint Verification

Competition) [21] and one dataset is obtained by webcam. The

information of each dataset is shown in Table 1. Each

fingerprint image allows a rotation angle that belongs to !"�/4,

�/4# (compared with the vertical line). Every two images from

one finger have an overlap of common region. But there may

be no delta points or core points in some fingerprint images.

Table1: The information of dataset

The source of

the datasets

Sensors

different

fingers

/ total

images

Image

size

Resolution

1st DB FVC2004 DB1 Optical sensor 110/880 640 x 480 500 dpi

2nd DB FVC2004 DB2 Optical sensor 110/880 328 x 364 500 dpi 3rd DB FVC2004 DB3 Thermal

sweeping sensor

110/880 300 x 480 512 dpi

4th DB FVC2004 DB4 SFinGe v3.0 110/880 288 x 384 500 dpi

5th DB Collected using

proposed

approach

Webcam 150/1200 640 x 480 450 dpi

B. Experiments Setup

We posed 2 experiments. For each experiment, we compared

the FAR and FRR of our webcam database with the rest of 4

database which are taken from FVC2004 using TSVM. Both

the experiments are done by the method of 5-folder cross

validation, but have differences in the size of test sets and

training sets.

Experiment 1. For database 1 to database 4, we have divided

880 images into 5 parts, each of which has 176 images. The

algorithm TSVM runs five times. For each time, four of the

five parts are used as training sets (our approach only), and the

other one part is used as test set. The averaged verification

result will be reported over these 5 times.

Experiment 2. For database 5, we have divided 1200 images

into 5 parts, each of which has 240 images. Then the algorithm

TSVM runs again five times. For each time, one of the five

parts is used as training set (our approach only), and the other

four parts are used as test sets. The averaged verification result

will be reported over these 5 times.


Fig. 7: Experiment setup structure using our proposed approach.

C. Results and Discussions

The performance of a fingerprint authentication system can be

measured by,

• Equal Error Rate (EER) on the training set, correspondent

to the error rate computed at the threshold value for which

the percentage of genuine users wrongly rejected by the

system (FRR) is equal to the percentage of impostors

wrongly accepted by the system (FAR);

• Generalisation errors, i.e., we computed FAR and FRR on

the test set using the EER threshold previously estimated.

• Total Error Rate (TER), which is the overall generalisation

error rate of the system computed at the EER threshold.

Our results are summarised by table 2 in terms of EER on the

training set (fourth column) and FAR and FRR on the test set

computed with the EER threshold previously estimated (second

and third columns). And we compare our webcam data with the

database of FVC2004.

As expected, the thermal sweeping sensor performs notably

worse than the others. This is mainly due to the reduced

sensing surface, which also reduces the number of extracted

minutiae. This also causes that multiple impressions of the

same fingerprints correspond to different parts of them, so the

extracted minutiae do not match each others. Webcam sensor

performs remarkably very closer to optical sensor using

proposed system.

Table 2. Errors of various kinds of sensors in fingerprint

verification systems. The EER is computed on the training set.

FAR and FRR are computed on the test set using the EER

threshold estimated from the training set.

Sensors FAR FRR EER

Optical 0.63% 2.41% 1.52%

Optical 0.95% 4.33% 2.64%

Thermal sweeping sensor 0.57% 6.92% 3.74%

SFinGe v3.0 0.51% 5.11% 2.81%

Webcam 0.43% 3.27% 1.85%

Table 2 presents the experimental results of db1 to db5 as well

as different types of sensors. We notice that our approach using

a pre-processing phase as well as gamma manipulation and

gamma correction with the conventional image enhancement

system really can achieve much better accuracy on behalf of

the poor quality image and low resolution image such as the

fingerprint images captured by webcam. As shown in Table 1,

fingerprints of these five datasets are confined by different

types of sensors. So the images have different size, resolutions

and qualities. This strappingly recommends that our proposed

scheme capture well the ridge information needed for

fingerprint authentication, and have a low influence by

fingerprint image quality. We perceive in experiment 2 that

although the proportion of training sets is reduced, and the

number of test members is increased in db5, our approach still

works better. This demands that this approach have a low

influence by fingerprint image amount. Comparing the

experimental results of all the other datasets with webcam

datasets, it turns out that our proposed scheme can detect better

feature data from poor and low resolution fingerprint images

and can provide good help to improve fingerprint matching

system. This is because pre-processing phase of our scheme

makes successful use of the matching vectors to increase

classification and to obtain a threshold selection range for

better precision. In table 2 the experimental results of the

Yes

/No

Train Image

Pre-processing Image

Enhancement

Feature

Extraction

Test Image

Fingerprint

Database

Matching

Pre-processing Image

Enhancement

Feature

Extraction Enrolment

Enrolment

Matching


different datasets are compared. And it is clear that the

accuracy of the webcam (i.e. low resolution datasets) datasets

using our proposed approach outperforms with the other

datasets consistently and significantly

IV. CONCLUSION

We formulated a webcam based fingerprint authentication for

personal identification system and presented experimental

results in this paper. Because of the fingerprint images

acquired with a webcam are low resolution and sometimes very

poor quality images; a novel approach was used. We called it

fingerprint pre-processing. Our key contribution of this paper

is, we established a pre-processing approach in addition to

gamma manipulation and gamma correction to adjust lightness

and intensities of the fingerprint image. To estimate the feature

locations we used TSVM matching algorithm, which will

evaluate the performance of our proposed scheme regarding

error rates. Our future work in progress is the implementation

of an optimal matching algorithm to test the quality of the

minutiae extraction. Of course, future work will include

porting the whole algorithm to the hardware coprocessor,

possibly using a soft-core processor for certain features. Also

the interface to the software is going to be ported to a dynamic

link library in order to make it accessible from within many

software development environments, such as Visual Basic,

Visual C/C++, Delphi, etc.

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[18] http://en.wikipedia.org/wiki/Fingerprint_authentication

[19] http://en.wikipedia.org/wiki/Webcam

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http://bias.csr.unibo.it/fvc2004/download.asp

Md. Rajibul Islam

[email protected]

Md. Shohel Sayeed

[email protected]


Andrews Samraj

[email protected]

Faculty of Information Science and Technology

(FIST)

Multimedia University, Jalan Ayer Keroh lama,

75450 Melaka, Malaysia


webcam based fingerprint authentication for personal identification system

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