fingerprint authentication system using a low-priced webcam

International Conference on Data Management (ICDM2008), IMT Ghaziabad, India, Feb. 25-26, 2008

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Fingerprint Authentication System using a low-priced Webcam

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

Faculty of Information Science and Technology (FIST)

Multimedia University, Jalan Ayer Keroh lama, 75450 Melaka, Malaysia

E-mail: {md.rajibul.islam05, shohel.sayeed, andrews.samraj}@mmu.edu.my

Abstract

A number of biometric techniques have been proposed for personal identification in the past.

Among the vision-based ones, we can point out fingerprint, face, palm, ear, iris and retina

recognition. Voice recognition or signature verification are the most widely known among

the non-vision based ones. Signature verification requires the use of electronic tablets or

digitizers for on-line capturing and optical scanners for on-line adaptation. These interfaces

have some negative aspect that they are large and convoluted to use, increasing the intricacy

of the whole identification system. On the other hand, scanners and cameras are much smaller

and easy to handle, and are becoming all over in the current computer atmosphere. Lots of

vision-based biometric techniques have been projected in the past for personal identification.

We present a novel one based on visual capturing of fingerprints using a Webcam. However,

there is an open issue to use webcam in stead of any scanner because of the low price,

available in the market and easy to adjust anywhere. In this paper, we describe our

implementation of the fingerprint authentication system using webcam having Pentium IV

CPU, 256 RAM and a piece of transparent glass and a light source. Also, we describe a

preprocessing technique based on gamma manipulation and gamma correction that can be

executed to adjust lightness and intensities of the fingerprint image before fingerprint image

enhancement and feature extraction.

Key words: webcam, fingerprint, gamma manipulation, gamma correction, fingerprint

authentication.

1.0 INTRODUCTION Because of ridge direction and minutiae such as ridge endings and ridge bifurcations are used

for matching, so the performance of automatic fingerprint matching systems depends on local

ridge characteristics. The ridges can be easily detected and minutiae can be correctly

extracted in an ideal fingerprint thinned image. However, the quality of many fingerprints is

often poor due to the injured part on the skin and the atmosphere in which it was taken. Also

the quality of fingerprints is very poor which are captured by a low priced webcam. The ridge

formations in these poor-quality fingerprint images are not well defined and minutiae cannot

be correctly detected. Therefore, a discrete ridge structure is necessary to assurance robust

minutiae detection in spite of image quality. As such, the goal of this research is to improve

the clarity of ridge structures of a poor fingerprint image captured by webcam to assist the

correct extraction of minutiae.

Enhancing fingerprint images facilitate matching is a problem that has been much studied [1]

[2] [3]. Minutiae extraction from fingerprint images is one of the most important steps in

automatic fingerprint identification and classification. Minutiae are local discontinuities in

the fingerprint pattern, mainly terminations and bifurcations [1]. Fingerprint image quality is

an important factor in the performance of Automatic Fingerprint Identification Systems

(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and

evaluate fingerprint sensors. [2]


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A number of techniques to enhance fingerprint images have been proposed, which take

advantage of ridge characteristics such as directionality. In this paper, we have presented a

preprocessing system using gamma manipulation and gamma correction methods which

adjust lightness and intensities of the poor quality fingerprint image. The rest of the paper is

prearranged as, in section 2 we present an overview of the proposed system and also our

contribution in this paper specially gamma manipulation and gamma correction. In section 3

we show the experiments, results and discussion and finally section 4 concludes the paper.

2.0 OVERVIEW OF OUR PROPOSED SYSTEM

Fig. 1 shows our proposed block diagram of the whole authentication system and the

experimental setup. The preprocessing stage performs the initialization of the algorithm, i.e. it

captures a colorful low resolution fingerprint image and convert it to grayscale image and

performs the gamma manipulation and gamma correction to adjust lightness and intensities of

the fingerprint image. Then feeds it into the next 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 [4]. Just before feature extraction a

thinning process needs to be performed as indicated in [5]. 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 [6], 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.

(a) Enrolment Process

(b) Verification Process

Fig. 1: (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

Yes/ No

Glass

Webcam

Preprocessing

stage

Fingerprint

enhancement

Feature

extraction

System

Database

Matching


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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.

2.1 Our Contribution

Our proposed scheme is the most automatic and unobtrusive security device. We use a

webcam attached to the computer and applies fingerprint recognition to analyze the

fingerprint it sees, matching it against the minutiae of the fingerprint belonging to the

authorized user. When the proposed protocol ready to capture fingerprint using USB-

connected webcam, we tried both, a relatively high end Creative unit and a low end Logitech

QuickCam and didn't notice any difference in performance. It seems that the fingerprint

recognition process does not depend on a particularly high resolution image to identify the

necessary features which we have presented below in this section. On the other hand, we did

notice a difference when changing lighting conditions. When the light source is behind the

fingerprint, our proposed scheme has more difficulty identifying the features than when the

lighting is in front or to the side. Another problem is to get the whole minutiae as well as

ridges and valleys of the fingerprint as shown in Fig 2.

(a) (b)

Fig. 2: (a) Fingerprint captured by webcam without using transparent glass. (b)

Enhanced fingerprint using the code loosely follows the approach presented by

Peter Kovesi [4].

To overcome this problem we have used a piece of clear transparent glass. The user has to

press his finger on the glass and from the opposite side the fingerprint will be captured. And

between the webcam and glass we have used light source lighting to the side because if the

lighting is in front of the glass it reflects and captured a shadow with the fingerprint image.

The lightness of the fingerprint images captured by low-priced webcam is so bright and blur,

and for that it doesn’t give better result to enhance the ridges of the fingerprint image. That’s

why we’ve used a preprocessing stage in our system to perform gamma manipulation and

correction to adjust lightness and intensities of the fingerprint image. The outcomes are

shown in Fig. 5 and Fig. 6.

2.2 Gamma Manipulation

The fingerprint images captured by webcam are not gamma corrected. During the

preprocessing stage when the image processing operations are performed on color fingerprint


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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 [8]. In this paper we

propose a standard method that allows lightness processing on grey image without exceeding

the limits of the gamut of the technique.

In such exercise it will be asked to display gray scale images in Matlab. In that case if P is an

image that takes on the values [0,1,……..,255], then it may be displayed by using the

following commands.

image(P+1);

axis('image');

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

colormap(graymap);

In this technique the lightness processing is a function of the color of the pixel 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.

The proposed method can be used to apply a variety of grey value algorithms on color

fingerprint images. In this section we show the results for contrast improvement using gamma

manipulation. The effect of a gamma manipulation is that the lightness values are distributed

nonlinearly over the range that is used. This may increase the contrast in one or more regions

of the lightness range, at the cost of decreasing the contrast in other regions. In fig. 3 the most

common form of gamma manipulation is shown, mathematically described by: γ

−

−−+=

*

min

*

max

*

min

**

min

*

max

*

min

* *)(LL

LLLLLLout …………………….(1)

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).


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Fig. 3: Gamma manipulation. The result of equation 1 is given for three different

gamma values. It can be seen that for γ < 1 the lightness of the image ( *

outL ) is

always higher than for the original image ( *

inL ), and that darker colors have more

contrast. For γ > 1 the opposite holds true.

2.3 Gamma Correction

We’ve used gamma correction to compensate for the nonlinear behavior of a displayed

fingerprint image. Most often images are already encoded in gamma corrected form when

anyone using a high quality Digital camera, and will appear fine when displayed on most

video monitors but for the fingerprint image captured by webcam has to encoded in gamma

corrected form before using the enhancement algorithm to obtain better result. 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 (2). γ

=

255255

nm .............................(2)

Where n is the original pixel value and m is the pixel intensity as it appears on the display.

This relationship is illustrated in Fig. 4.

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.


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Fig. 4: 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 non-linear. 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) ………………(3)

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 ………………(4)

(a) (b) (c) (d)

Fig. 5: (a) Image captured by webcam, (b) Grayscale conversion, (c) Shown the

results of gamma manipulation and inverting the gamma correction, (d) Enhance

part of fingerprint-c.


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(a) (b)

Fig. 6: (a) Image enhancement before preprocessing, (b) Image enhancement after

preprocessing

3.0 EXPERIMENTS AND DISCUSSIONS

We conduct experiments with data of fingerprint verification competitions, to demonstrate

the advantages of our proposed approach to fingerprint verification using low-priced

webcam.

3.1 Datasets

We used an improved fingerprint matching approach using TSVM [7] in order to prove the

influence of different image qualities and image amount to our proposed approach, we have

collected 5 datasets and within these four datasets from FVC2004 (The Second International

Fingerprint Verification Competition) and one dataset which is obtained using our proposed

scheme. The information of each dataset is shown in Table 1. Each fingerprint image allows a

rotation angle that belongs to [−π/4, π/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.

3.2 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, 880 images are divided 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, 1200 images are divided 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


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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.

3.3 Measures

The performance of our proposed fingerprint authentication system can be measured by FRR

(False Rejection Rate: each sample in the subset A is matched against the remaining samples

of the same finger), FAR (False Acceptance Rate: the first sample of each finger in the subset

A is matched against the first sample of the remaining fingers in A). The configuration of

running computer is Pentium IV CPU, 256 RAM.

3.4 Results

Table 1. Experimental results of db1 to db5 using TSVM

The source of

the datasets

Sensors

Image size

Resolution

FAR FRR

1st DB FVC2004 DB1 Optical sensor 640 x 480 500 dpi 0.064% 1.98%

2nd

DB FVC2004 DB2 Optical sensor 328 x 364 500 dpi 0.094% 8.87%

3rd

DB FVC2004 DB3 Thermal

sweeping

sensor

300 x 480 512 dpi 0.057% 6.18%

4th

DB FVC2004 DB4 SFinGe v3.0 288 x 384 500 dpi 0.059% 5.77%

5th

DB Collected using

proposed approach

Webcam 640 x 480 450 dpi 0.042% 3.92%

The experimental results of db1 to db5 are shown in Table 1. We see that our proposed

authentication system using a low priced webcam really can achieve much better accuracy

which we compared by the FAR and FRR rate using the matching approach TSVM. As

shown in Table 1, fingerprints of the five datasets are captured by sensors of different types.

So the images have different qualities. This strongly suggests that our proposed scheme as

well as preprocessing system, image enhancement, feature extraction and TSVM methods

capture well the information needed for fingerprint verification, and have a low influence by

fingerprint image quality. We see in experiment 2, that although the proportion of training

sets is reduced, and the number of test members is increased in db5, our proposed approach

using a low priced webcam as a sensor still works better than the authentication system using

expensive sensors which are available in the market.

This implies that this approach has a low influence by fingerprint image amount. Comparing

the experimental results of all the other datasets with webcam datasets using our approach, it

turns out that the transductive learning technique can provide some help to fingerprint

authentication system. We think this is because our preprocessing phase of the proposed

scheme makes effective use of the matching vectors to enhance classification and to derive a

threshold selection range for better accuracy. The experimental results of our webcam

datasets obtained by the proposed approach are compared in Table 1. It is clear that the

accuracy of the webcam datasets using our proposed approach outperforms with the other

datasets consistently and significantly.

4.0 CONCLUSION

In this paper, a fingerprint preprocessing approach using a webcam was proposed. Since the

characteristics of fingerprint images acquired with a webcam are quite different from those


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acquired by conventional touch-based sensors, a new fingerprint preprocessing algorithm was

used. The main contributions of this paper are: First, we introduced a fingerprint acquisition

model incorporating gamma manipulation and gamma correction during preprocessing that

can be executed to adjust lightness and intensities of the fingerprint image before fingerprint

image enhancement and feature extraction. We have found that this clearly outperforms

defining the features from a very low quality fingerprint captured by a low priced webcam.

Second, we used a fast TSVM matching algorithm to estimate the feature locations, which

will evaluate the performance of our proposed approach by the FAR and FRR rate of the

webcam datasets with the datasets obtained by other sensors.

REFERENCE

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[3] O'Gorman, L. Nickerson, J.V. “Matched filter design for fingerprint image

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[4] P.D. Kovesi. Matlab functions for computer vision and image analysis.

http://www.csse.uwa.edu.au/~pk/Research/MatlabFns/index.html.

[5] 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

[6] C.Arcelli and G.S.D.Baja, “A Width Independent Fast Thinning Algorithm,” IEEE Trans.

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[7] Jia Jia, Lianhong Cai, “A TSVM-Based Minutiae Matching Approach for Fingerprint

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[8] J. Dijk and P.W. Verbeek, “Lightness Filtering in Color Images with Respect to the

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fingerprint authentication system using a low-priced webcam

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