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, web- cam, 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.

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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:

Q ………(1) V A C =∈o ∈r ………..(2) d C=

Where C is the capacitance in farads Q is the charge in coulombs V is the potential in volts İ0 is the permittivity of free space, measured in farad per meter İr 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 nonconductive epidermal layer acts as a dielectric.

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.

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

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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.

(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

(a)

(b)

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

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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.

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).

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

Glass

System Database

(a)

Feature extraction

Webcam

Fingerprint enhancement

Preprocessing stage

(b) Yes/ No

Matching

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

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

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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.

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).

D. Gamma Manipulation in Fingerprint image

Ȗ = 0.5 Ȗ = 1.0 Ȗ = 2.0

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.

L*out

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);

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 Ȗ < 1 the *

lightness of the image ( Lout ) is always higher than for the original image

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:

* out

L

* min

=L

* max

+ (L

* min

−L

§ L* − L*min ) * ¨¨ * * © Lmax − Lmin

γ

· ¸¸ (3) ¹

L* and L*out are the input and output lightness and L*min and * max

L

are the minimum and maximum of the lightness range.

When this manipulation is used with

γ

L*in

>1, the higher (lighter)

*

( Lin ), and that darker colors have more contrast. For Ȗ > 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 C < λCˆ out ( L , h ) or Cˆ in ( L , h ) < Cˆ out ( L do nothing, else * * * * * * α ( Lcusp (h ) − L )(C − λCˆ out ( L , h )) L* mod = L* + * * 100 C ref − λCˆ out ( L , h ) *

*

*

*

* C mod = λCˆ out ( L*mod , h * ) + (1 − λ )Cˆ out ( L*mod , h * )

where Cˆ in ( L , h the image and *

*

*

*

, h * ) then

C * − λCˆ out ( L*mod , h * ) ˆ C in ( L* , h * ) − λCˆ out ( L*mod , h * )

(4) (5)

) and Cˆ out ( L* , h* ) are the boundaries of the

reproduction

gamuts,

respectively;

L*cusp (h* ) is the lightness of the cusp at a given hue angle. Cref is a parameter that influences the curvature of the mapping direction and must be greater than the largest possible chroma, e.g.

128 2 for TIFF-CIELAB images; λ is the

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degree of soft clipping and change (0-100%).

α

gives the degree of lightness

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.

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 gamutlimited 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

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. Displayed _ Intensity = pixel _ value ∧ gamma .

L* for this particular

C * value. (b)

C * / L* = constant, the lightness is manipulated while

keeping the ratio

C * / L* constant. The minimum value is per

definition 0, the maximum value is the lightness value for which the line

C * / L* 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 Ȗ = 2 and Ȗ = 0.5 and we obtained good outcome for Ȗ=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).

§ q · p = 255¨ ¸ © 255 ¹

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:

pixel = P ∧ (1 / G )

(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. pixel = (( P / MaxIntensity ) ∧ (1 / G )) ∗ MaxIntensity (8)

γ

(6) (a)

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(b)

Islam, Sayeed & Samraj 7

Table1: The information of dataset The source of the datasets

Sensors

1st DB 2nd DB 3rd DB

FVC2004 DB1 FVC2004 DB2 FVC2004 DB3

4th DB 5th DB

FVC2004 DB4 Collected using proposed approach

Optical sensor Optical sensor Thermal sweeping sensor SFinGe v3.0 Webcam

different fingers / total images 110/880 110/880 110/880

Image size

Resolution

640 x 480 328 x 364 300 x 480

500 dpi 500 dpi 512 dpi

110/880 150/1200

288 x 384 640 x 480

500 dpi 450 dpi

B. Experiments Setup (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.

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.

(a)

(b)

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

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.

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

!"

ʌ/4,

ʌ/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.

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Pre-processing

Train Image

Image Enhancement

Feature Extraction

Enrolment

Fingerprint Database

Matching

Pre-processing

Image Enhancement

Enrolment

Feature Extraction

Matching

Yes /No

Test Image 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 Optical Optical Thermal sweeping sensor SFinGe v3.0 Webcam

FAR 0.63% 0.95% 0.57% 0.51% 0.43%

FRR 2.41% 4.33% 6.92% 5.11% 3.27%

EER 1.52% 2.64% 3.74% 2.81% 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

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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.

REFERENCE [1] P.D. Kovesi. Matlab functions for computer vision and image analysis. http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/inde x.html. [2] T. Y. Zhang , C. Y. Suen, “A fast parallel algorithm for thinning digital patterns”, Communications of the ACM, vol.27 n.3, p.236-239, March 1984 [3] C.Arcelli and G.S.D.Baja, “A Width Independent Fast Thinning Algorithm,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 7, no. 4, pp. 463-474, 1984. [4] Jia Jia, Lianhong Cai, “A TSVM-Based Minutiae Matching Approach for Fingerprint Verification.” Lecture Notes in Computer Science. Springer Berlin / Heidelberg. IWBRS 2005, vol. 3781, pp. 85-94, 2005. [5] J. Dijk and P.W. Verbeek, “Lightness Filtering in Color Images with Respect to the Gamut”, CGIV 2006, Proc. Third European Conference on Colour in Graphics, Imaging, and Vision (University of Leeds, UK, June 1922), 2006, pp. 330-335. [6] Anil K. Jain and David Maltoni. Handbook of Fingerprint Recognition. Springer-Verlag New York, Inc, 2003. [7] Md. Rajibul Islam, Md. Shohel Sayeed, Andrews Samraj, “Precise Fingerprint Enrolment through Projection Incorporated Subspaces based on Principal Component Analysis (PCA), in Proc. 2nd International Conference on

Informatics (Informatics 2007), Kuala Lumpur, Malaysia, Nov. 27-28, 2007, pp. T1 (85-91). [8] G. Williams, “IRIS recognition technology", IEEE AES system Magazine, pp. 23-29, April 1997. [9] Daugman, “Recognizing Persons by their Iris Patterns”, In Biometrics: Personal Identification in Networked Society, Kluwer, pp.103-121, 1998. [10] Guiyu Feng, Dewen Hu, Ming Li and Zongtan Zhou, “Palmprint Recognition Based on Unsupervised Subspace Analysis”, Lecture notes in Computer Science. Springer Berlin / Heidelberg, vol. 3610/2005, pp. 675-678, July 27, 2005. [11] Qingyun Dai, Ning Bi, Daren Huang, Dvaid Zhang, Feng Li, “M-band wavelets application to palmprint recognition based on texture features”, International Conference on Image Processing, ICIP2004. Vol. 2, pp. 893 – 896, 24-27 Oct. 2004. [12] Jie Wu, Zhengding Qiu, "A Hierarchical Palmprint Identification Method Using Hand Geometry and Grayscale Distribution Features", 18th International Conference on Pattern Recognition (ICPR'06), pp. 409412, 2006. [13] Francisco Martinez, Carlos Orrite and Elias Herrero, “Biometric Hand Recognition Using Neural Networks”, Lecture notes in Computer Science. Springer Berlin / Heidelberg, vol.3512/2005, pp. 1164-1171, June 21, 2005. [14] J. Zhang, Y. Yan, and M. Lades, "Face Recognition: Eigenface, Elastic Matching and Neural Nets”, Proceedings of IEEE, vol. 85, no. 9, pp. 1423-1435, Sept. 1997. [15] R. Brunelli, T. Poggio, “Face Recognition: Features versus Templates”, IEEE Trans. on PAMI, Vol. 15, No. 10, pp. 1042-1052, Oct. 1993. [16] J.Picone, “Duration in Context Clustering for Speech Recognition”, Speech Communication, Vol.9, pp. 119-128, 1990. [17] Picone, J.W, “Signal modeling techniques in speech recognition”, Proceedings of the IEEE, Vol. 81, pp. 1215 – 1247, Sep 1993. [18] http://en.wikipedia.org/wiki/Fingerprint_authentication [19] http://en.wikipedia.org/wiki/Webcam [20] Lindsay W. MacDonald and M. Ronnier Luo. Colour Image Science Exploiting Digital Media. John Wiley & Sons, Ltd. The Atrium, Southern Gate, Chichester, West Sussex, England. [21] FVC2004 website, http://bias.csr.unibo.it/fvc2004/download.asp

Md. Rajibul Islam [email protected] Md. Shohel Sayeed [email protected]

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Andrews Samraj [email protected] Faculty of Information Science and Technology (FIST) Multimedia University, Jalan Ayer Keroh lama, 75450 Melaka, Malaysia

College Science in India 1 : 3 December 2007 Webcam Fingerprint Personal Identification

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