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
a_noori_h@yahoo.com rs@ftsm.ukm.my

Afsaneh Noori Hoshyar
Department of Industrial Computing
University Kebangsaan Malaysia
Bangi, Malaysia
Afsa1986@yahoo.com

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 Vein patterns are located inside the body. Therefore, it
difficult. Therefore, the finger vein authentication systems have provides a high level of accuracy due to the uniqueness and
received extensive attention in public security and information complexity of vein patterns of the finger. It is difficult to forge.
security domains. According to the importance of these systems, Epidermis status cannot effect on recognition system [2].
the different techniques have been proposed to each stages of the Finger vein systems provide user-friendly environment.
system. The stages include image acquisition, preprocessing, Therefore, finger vein is a good candidate for authentication
segmentation and feature extraction, matching and recognition. and security purposes.
While the segmentation techniques often appear feasible in
theory, deciding about the accuracy in a system seems important. According to the importance of finger vein authentication
Therefore, this paper release the conceptual explanation of finger system, this paper proposes a system as shown in figure1.
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 personal Figure 1. The scheme of finger vein authentication system
authentication schemes to confirm or identify an individual In the proposed system, different filters are applies for pre-
requesting their services. The purpose of these schemes is processing stage. Since there are different techniques on
ensuring that only a legal user and no one else can access to segmentation stage of authentication systems such as matched
provider services. Among different authentication traits such as filter [3], morphological methods [4], repeated line tracking
fingerprints, hand geometry, vein, facial, voice, iris and method [5] and maximum curvature points in image profiles
signature, finger vein authentication is a new biometric [6], the lack of experiment on combining two different
identification technology using the fact that different person techniques of “gradient-based threshold” and “maximum
has a different finger vein patterns. The idea using vein patterns curvature points in image profile” was found to improve the
as a form of biometric technology was first proposed in 1992, quality of verification system, while the previous studies
while researches only paid attentions to vein authentication in considered just a single technique for segmentation purpose. In
last ten years. Vein patterns are sufficiently different across next step, Neural Network is applied to evaluate the quality of
individuals, and they are stable unaffected by ageing and no training and testing, finally Neural Network is trained and
significant changed in adults by observing. It is believed that tested for pattern recognition purpose.
the patterns of blood vein are unique to every individual, even
among twins [1].

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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 7, July 2011
Experimental results of this work show that the system is
valid for user authentication purpose even in high security Input image
environments, as it was the initial intention given the nature of
human finger vein.

II. FINGER VEIN AUTHENTICATION SYSTEM cropping

The steps of finger vein authentication system are explained in
the following.
Reducing noise
A. Image Acquisition
The first step in finger vein authentication system is
capturing the image of finger veins. The quality of captured
image helps to identify the veins of fingers as well. Image Increasing contrast
Acquisition can be done in two ways; i) using infrared-
sensitive digital camera with wavelength between 700nm to Figure 3. Image enhancement process
1000nm and banks of LEDs ; ii) using digital camera with
CCD sensor and IR filter which is located on the camera with C. Segmentation and Feauture extractions
wavelength 700nm to 1000nm and banks of LEDs . In this stage, the enhanced finger vein image is segmented
Therefore, as shown in figure 2, the Near-infrared rays and the features are extracted. Since there are different methods
generated from a bank of LEDs (light emitting diodes) for segmentation, this paper propose the combination of two
penetrate the finger and are absorbed by the hemoglobin in the segmentation methods as "Gradient-based thresholding using
blood. The areas in which the rays are absorbed (veins) thus morphological operation" and "Maximum Curvature Points in
appear as dark areas in an image taken by a CCD camera Image Profiles" to segment and extract the features. The
(charge-coupled device) located on the opposite side of the features of first segmentation method are merged with features
finger. The CCD camera image will be transferred to PC for of second segmentation method to obtaine an accurate record
next step of authentication [7]. for each finger vein images.
1) Gradient-based thresholding using morphological
operation: In this segmentation method, the gradient of
image by alpha filter is created. Then, thresholding is
performed on gradient of image. The high gradient values
which are more than threshold value in the image fall as
edge (vein). After the vein determination in an image, the
morphological operations are employed to make an image
smoother. The proposed morphological operations are
„majority‟ to remove extra pixels, „openning‟ to smooths
Figure 2. Image Acquisition system[8] the contour of image and breaks narrow passages, „bridge‟
to connects the neighbor pixels which are disconnected.
As stated above, the better quality can make recognition The original and obtained image after first segmentation
system more accurate. For this purpose, the noise is reduced on method (includes performing gradient, thresholding,
next step. morphological operation) are shown in figure 4.
B. Pre-Processing
As the image has been taken by camera has redundant parts
which needs to be cropped. Therefore, only the central part of
finger vein image can be taken in Matlab by a simple line;

I2= imcrop (I, rect); (1)

Where „I‟ is an image and „rect‟ is the position for
cropping.
a b
The next step in this section is reducing the noise of finger
Figure 4. a) Original image b) Obtained image after first
vein image to improve segmentation. Since the captured image segmentation
has much noise, therefore it needs to be improved for getting
better quality. For this purpose, the enhancement Functions The total process for the "Gradient-based thresholding
such as ‟medfilt2‟, „medfilt2‟ can be employed. As the final using morphological operation" method is shown as figure 5.
step for image pre-processing, the image contrast can be
increased using commands in Matlab such as „histeq‟. Figure 3
shows the total process for enhancing the image.

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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 7, July 2011
Gradient of image

The total process for the "Maximum Curvature Points in
Thresholding Image Profiles” method is shown as figure 5.
Calculating the cross
sectional profile of image
'majority' operation

calculating curvatures
'open' operation

Calculating the Local
'bridge' operation maximum of curvatures

Segmented image Calculating the scores of
center points

Figure 5.Total process of first method for finger vein extraction
Assigning scores to center
2) Maximum Curvature Points in Image Profiles: In this points
segmentation method, the curvatures of image profiles are
checks, then, the centerlines of veins are obtained by Figure 5. Total process of second method for finger vein extraction
considering the positions where the curvatures of a cross- The following features in the first and second methods of
sectional profile are locally maximal. The centerlines are "Gradient-based thresholding using morphological operation"
connected to each other; finally, the vein pattern is achieved. and "Maximum Curvature Points in Image Profiles" are
This method is robust against temporal fluctuations in vein extracted to train Neural Network.
width and brightness (N.Miura, A.Nagasaka, and T.Miyatake
2005). The extracted features for the first method are as follows.
The algorithm for achieving the pattern can be divided into 2  Sum(~BW2(:)) : The number of black pixels in the
stages; segmented vein image.
 Extracting the centreline positions of veins: The first step of  Bwperim(BW2): Perimeter of foreground(veins) in
algorithm is to detect the centerline positions. For this segmented vein image.
purpose, the cross-sectional profile of finger vein image is
calculated to obtain the intensity value of each pixel along the  Bwdist(BW2): Number to each pixel that is the distance
line in an image. In created matrix of intensity, when the between the pixel and the nearest nonzero pixel of BW2.
intensity is positive, it is considered as curvature until it  Bwarea(BW2): The area of the foreground (veins) in
becomes negative again. The maximum differences of segmented vein image.
intensities between two pixels are considered as a vein pixel
in a row of matrix.
 Connecting center positions of veins: For connecting the The extracted features for the second method are as follows.
center positions, all the pixels are checked. If a pixel and two  Cross sectional profile of segmented vein image in
neighbors in both sides have large values, the horizontal line vertical direction: Sum of the intensities of pixels in
is drawn. If a pixel and two neighbors in both sides have segmented vein image in vertical direction.
small values, a line is drawn with a gap at a pixel position.
Therefore, the value of a pixel should be increased to connect  Cross sectional profile of segmented vein image in
the line. The last condition on connecting the center positions horizental direction: Sum of the intensities of pixels in
of veins is a pixel has large value and two neighbors in both segmented vein image in horizental direction.
sides have small values, a dot of noise is created in pixel
position, and therefore the value of a pixel should be reduced.  Cross sectional profile of segmented vein image in
Figure 6 shows the result of second segmentation. oblique1 direction: Sum of the intensities of pixels in
segmented vein image in oblique1 direction.
 Cross sectional profile of segmented vein image in
oblique2 direction: Sum of the intensities of pixels in
segmented vein image in oblique2 direction.
 Curvatures score: Sum of the calculated scores of
curvatures in segmented vein image.

a b
D. Matching and Recognition by Neural Network
The table which is created using the combination of
Figure 6. a) Original image b) Obtained image after second segmentation
"Gradient-based thresholding using morphological operation"

17 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 7, July 2011
and "Maximum Curvature Points in Image Profiles" methods is
applied for training Neural Network and also estimating the
quality of training and testing for proposed model. This assess
is done by comparing the true output and the output of the
model.
For training Neural Network the table is divided into two
tables of training and testing. Therefore, training table has data
are used for training purpose and testing table has data are used
for testing purpose. Also another two tables are created as
training output and testing output. The data of training and
testing output considered as the name of image. Therefore, the
Neural Network has been trained and then simulated to assess
the model quality by comparing the true output and the model
output.
In training Neural Network, the epochs and goal were
considered „200000‟ and „0‟. The best run occurred when the
performance become close to the goal. As figure 6, the
performance becomes „0.183054‟ from „200000‟ which is close
to the goal „0‟.

Figure 8. The VAF index for training and testing

After training, the Neural Network is simulated using the
simulation 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

Figure 6. Training process After simulation the R is obtained as Figure 9.
The result of this trainig are shown as figure 7. It shows the
differences 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 output
in testing

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

Figure 9. Simulation result

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

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 7, July 2011
recognition can be employed in different applications of Conference on Bioinformatics and Biomedical Engineering, ICBBE
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