a combined method for finger vein authentication system

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(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 9, No. 7, July 2011

A Combined Method for Finger Vein Authentication

System

Azadeh Noori Hoshyar Assoc. Prof. Dr. Ir.Riza Sulaiman

Department of Computer Science Department of Industrial Computing

University Kebangsaan Malaysia University Kebangsaan Malaysia

Bangi, Malaysia Bangi, Malaysia

[email protected] [email protected]

Afsaneh Noori Hoshyar

Department of Industrial Computing

University Kebangsaan Malaysia

Bangi, Malaysia

[email protected]

Abstract — Finger vein as a new biometric is developing in

security purposes. Since the vein patterns are unique between

each individual and located inside the body, forgery is extremely

difficult. Therefore, the finger vein authentication systems have

received extensive attention in public security and information

security domains. According to the importance of these systems,

the different techniques have been proposed to each stages of the

system. The stages include image acquisition, preprocessing,

segmentation and feature extraction, matching and recognition.

While the segmentation techniques often appear feasible in

theory, deciding about the accuracy in a system seems important.

Therefore, this paper release the conceptual explanation of finger

vein authentication system by combining two different techniques

in segmentation stage to evaluate the quality of the system. Also,

it applies Neural Network for authentication stage. The result of

this evaluation is 95% in training and 93% in testing.

Keywords- Finger Vein authentication; Vein recognition;

Verification; Feature extraction; segmentation

I. INTRODUCTION

A wide variety of systems require the reliable personalauthentication schemes to confirm or identify an individualrequesting their services. The purpose of these schemes isensuring that only a legal user and no one else can access toprovider services. Among different authentication traits such asfingerprints, hand geometry, vein, facial, voice, iris and

signature, finger vein authentication is a new biometricidentification technology using the fact that different personhas a different finger vein patterns. The idea using vein patternsas a form of biometric technology was first proposed in 1992,while researches only paid attentions to vein authentication inlast ten years. Vein patterns are sufficiently different acrossindividuals, and they are stable unaffected by ageing and nosignificant changed in adults by observing. It is believed thatthe patterns of blood vein are unique to every individual, evenamong twins [1].

Vein patterns are located inside the body. Therefore, itprovides a high level of accuracy due to the uniqueness andcomplexity of vein patterns of the finger. It is difficult to forge.Epidermis status cannot effect on recognition system [2].Finger vein systems provide user-friendly environment.Therefore, finger vein is a good candidate for authenticationand security purposes.

According to the importance of finger vein authenticationsystem, this paper proposes a system as shown in figure1.

Figure 1. The scheme of finger vein authentication system

In the proposed system, different filters are applies for pre-processing stage. Since there are different techniques onsegmentation stage of authentication systems such as matchedfilter [3], morphological methods [4], repeated line trackingmethod [5] and maximum curvature points in image profiles[6], the lack of experiment on combining two differenttechniques of “gradient- based threshold” and “maximumcurvature points in image profile” was found to improve thequality of verification system, while the previous studiesconsidered just a single technique for segmentation purpose. Innext step, Neural Network is applied to evaluate the quality of training and testing, finally Neural Network is trained andtested for pattern recognition purpose.

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Vol. 9, No. 7, July 2011

Experimental results of this work show that the system isvalid for user authentication purpose even in high securityenvironments, as it was the initial intention given the nature of human finger vein.

II. FINGER VEIN AUTHENTICATION SYSTEM

The steps of finger vein authentication system are explained in

the following.

A. Image Acquisition

The first step in finger vein authentication system iscapturing the image of finger veins. The quality of capturedimage helps to identify the veins of fingers as well. ImageAcquisition can be done in two ways; i) using infrared-sensitive digital camera with wavelength between 700nm to1000nm and banks of LEDs ; ii) using digital camera withCCD sensor and IR filter which is located on the camera withwavelength 700nm to 1000nm and banks of LEDs .

Therefore, as shown in figure 2, the Near-infrared raysgenerated from a bank of LEDs (light emitting diodes)penetrate the finger and are absorbed by the hemoglobin in the

blood. The areas in which the rays are absorbed (veins) thusappear as dark areas in an image taken by a CCD camera(charge-coupled device) located on the opposite side of thefinger. The CCD camera image will be transferred to PC fornext step of authentication [7].

Figure 2. Image Acquisition system[8]

As stated above, the better quality can make recognitionsystem more accurate. For this purpose, the noise is reduced onnext step.

B. Pre-Processing

As the image has been taken by camera has redundant partswhich needs to be cropped. Therefore, only the central part of finger vein image can be taken in Matlab by a simple line;

(1)I2= imcrop (I, rect); Where „I‟ is an image and „rect‟ is the position for

cropping.

The next step in this section is reducing the noise of fingervein image to improve segmentation. Since the captured imagehas much noise, therefore it needs to be improved for gettingbetter quality. For this purpose, the enhancement Functionssuch as ‟medfilt2‟, „medfilt2‟ can be employed. As the finalstep for image pre-processing, the image contrast can beincreased using commands in Matlab such as „histeq‟. Figure 3shows the total process for enhancing the image.

cropping

Reducing noise

Increasing contrast

Input image

Figure 3. Image enhancement process C. Segmentation and Feauture extractions

In this stage, the enhanced finger vein image is segmentedand the features are extracted. Since there are different methodsfor segmentation, this paper propose the combination of twosegmentation methods as "Gradient-based thresholding using

morphological operation"

and"Maximum Curvature Points inImage Profiles" to segment and extract the features. The

features of first segmentation method are merged with featuresof second segmentation method to obtaine an accurate recordfor each finger vein images.

1) Gradient-based thresholding using morphologicaloperation: In this segmentation method, the gradient of image by alpha filter is created. Then, thresholding isperformed on gradient of image. The high gradient valueswhich are more than threshold value in the image fall asedge (vein). After the vein determination in an image, themorphological operations are employed to make an imagesmoother. The proposed morphological operations are„majority‟ to remove extra pixels, „openning‟ to smooths

the contour of image and breaks narrow passages, „bridge‟to connects the neighbor pixels which are disconnected.The original and obtained image after first segmentationmethod (includes performing gradient, thresholding,morphological operation) are shown in figure 4.

a b

Figure 4. a) Original image b) Obtained image after firstsegmentation

The total process for the "Gradient-based thresholdingusing morphological operation" method is shown as figure 5.

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Thresholding

'majority' operation

'open' operation

'bridge' operation

Gradient of image

Segmented image

Figure 5.Total process of first method for finger vein extraction

2) Maximum Curvature Points in Image Profiles: In thissegmentation method, the curvatures of image profiles arechecks, then, the centerlines of veins are obtained byconsidering the positions where the curvatures of a cross-sectional profile are locally maximal. The centerlines areconnected to each other; finally, the vein pattern is achieved.

This method is robust against temporal fluctuations in veinwidth and brightness (N.Miura, A.Nagasaka, and T.Miyatake2005).

The algorithm for achieving the pattern can be divided into 2stages;

Extracting the centreline positions of veins: The first step of algorithm is to detect the centerline positions. For thispurpose, the cross-sectional profile of finger vein image iscalculated to obtain the intensity value of each pixel along theline in an image. In created matrix of intensity, when theintensity is positive, it is considered as curvature until itbecomes negative again. The maximum differences of intensities between two pixels are considered as a vein pixel

in a row of matrix.

Connecting center positions of veins: For connecting thecenter positions, all the pixels are checked. If a pixel and twoneighbors in both sides have large values, the horizontal lineis drawn. If a pixel and two neighbors in both sides havesmall values, a line is drawn with a gap at a pixel position.Therefore, the value of a pixel should be increased to connectthe line. The last condition on connecting the center positionsof veins is a pixel has large value and two neighbors in bothsides have small values, a dot of noise is created in pixelposition, and therefore the value of a pixel should be reduced.Figure 6 shows the result of second segmentation.

a b

Figure 6. a) Original image b) Obtained image after second segmentation

The total process for the "Maximum Curvature Points inImage Profiles” method is shown as figure 5.

Calculating the crosssectional profile of image

Calculating the scores ofcenter points

calculating curvatures

Calculating the Localmaximum of curvatures

Assigning scores to centerpoints

Figure 5. Total process of second method for finger vein extraction

The following features in the first and second methods of "Gradient-based thresholding using morphological operation" and "Maximum Curvature Points in Image Profiles" areextracted to train Neural Network.

The extracted features for the first method are as follows.

Sum(~BW2(:)) : The number of black pixels in thesegmented vein image.

Bwperim(BW2): Perimeter of foreground(veins) insegmented vein image.

Bwdist(BW2): Number to each pixel that is the distancebetween the pixel and the nearest nonzero pixel of BW2.

Bwarea(BW2): The area of the foreground (veins) insegmented vein image.

The extracted features for the second method are as follows.

Cross sectional profile of segmented vein image invertical direction: Sum of the intensities of pixels insegmented vein image in vertical direction.

Cross sectional profile of segmented vein image inhorizental direction: Sum of the intensities of pixels insegmented vein image in horizental direction.

Cross sectional profile of segmented vein image inoblique1 direction: Sum of the intensities of pixels insegmented vein image in oblique1 direction.

Cross sectional profile of segmented vein image inoblique2 direction: Sum of the intensities of pixels insegmented vein image in oblique2 direction.

Curvatures score: Sum of the calculated scores of curvatures in segmented vein image.

D. Matching and Recognition by Neural Network

The table which is created using the combination of "Gradient-based thresholding using morphological operation"


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Vol. 9, No. 7, July 2011

and "Maximum Curvature Points in Image Profiles" methods isapplied for training Neural Network and also estimating thequality of training and testing for proposed model. This assessis done by comparing the true output and the output of themodel.

For training Neural Network the table is divided into twotables of training and testing. Therefore, training table has dataare used for training purpose and testing table has data are used

for testing purpose. Also another two tables are created astraining output and testing output. The data of training andtesting output considered as the name of image. Therefore, theNeural Network has been trained and then simulated to assessthe model quality by comparing the true output and the modeloutput.

In training Neural Network, the epochs and goal wereconsidered „200000‟ and „0‟. The best run occurred when theperformance become close to the goal. As figure 6, the performance becomes „0.183054‟ from „200000‟ which is closeto the goal „0‟.

Figure 6. Training process

The result of this trainig are shown as figure 7. It shows thedifferences between output and actual output in training and

testing.The blue line is output and the red line is actual output.

a b

Figure 7 a) Output and actual output in training b) Output and actual outputin testing

The Variance Accounted For (VAF) index which use toassess the quality of the model is estimated as 95% for trainingand 92% for testing as shown in figure 8 .

Figure 8. The VAF index for training and testing

After training, the Neural Network is simulated using thesimulation command in Matlab as the following.

R=sim(net,Features)

R= The result of Network simulation

Net = Created Neural Network

Features = obtained features from previous section

After simulation the R is obtained as Figure 9.

Figure 9. Simulation result

R=7.1756‟ shows the image belongs to the 7th person in thetable which was trained in Neural Network. Therefore, Rrecognizes the person who is dealing with a system. This


ISSN 1947-5500

a combined method for finger vein authentication system

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