Review on Finger Vein Authentication System by

Applying Neural Network

Azadeh Noori Hoshyar

Department of Computer Science, Faculty of Information Science and Technology,

University Kebangsaan Malaysia,

43600 UKM Bangi,Malaysia

a _ noori_ h@yahoo.com

Abstract- Biometric technologies are automated methods for

recognizing individuals based on biological and behavioral

characteristics. Establishing human identity, reliably and

conveniently has become a major challenge for a modern-day

society. Biometric technologies developed in response to the

growing worldwide demand for automated human identification

include finger, face, hand, iris, and other identifiers. Therefore,

this paper reviews the system based on finger vein leads towards

authentication of an individual. This system capture image of

finger vein by shining a near-infrared light, then some image

processing is done on vein image and finally trained Back

propagation Neural Network (BPN) verify whether the person is

authorized.

Keywords - Biometric, Authentication, Identification, Finger Vein.

I. INTRODUCTION

Personal identification technology is becoming more important in security systems. Traditionally, the authentication mode such as key; password; magnetic card are not safe enough because they could be stolen or easily forgotten. To ensure higher security, biometric technology has been applied to a wide range of systems including door control systems, public systems and pc login [1].

Biometrics is an authentication mechanism that relies on the automated identification or verification of an individual based on unique physiological or behavioral characteristics. Physiological characteristics refer to inherited traits that are formed in the early embryonic stages of human development [2]. It has high security and reliability because biometric features are hard to replicate and stolen. As well as security, the convenience of biometric verification system is becoming significant in daily life.

Biometric characteristics include fingerprint, face, iris, retina, signature, gait, voice, hand vein, hand/finger geometry and DNA information, while fingerprint, face, iris and signature are considered as traditional ones [3]. Finger vein recognition is a new biometric identification technology using the fact that different person has a different finger vein pattern

978-1-4244-6716-7/10/$26.00 ©2010 IEEE

Riza S ulaiman

Department of Industrial Computing, Faculty of Information Science and Technology,

University Kebangsaan Malaysia,

43600 UKM Bangi,Malaysia

rs@ftsm.ukm.my

[3]. Vein pattern is the network of blood vessels beneath person's skin. The idea using vein patterns as a form of biometric technology was first proposed in 1992, while researches only paid attentions to vein authentication in last ten years [3]. Vein patterns are sufficiently different across individuals, and they are stable unaffected by ageing and no significant changed in adults by observing. It is believed that the patterns of blood vein are unique to every individual, even among twins [3].

Contrasting with other biometric traits, such as face or fingerprint, vein patterns provide a really specific that they are hidden inside of human body distinguishing them from other forms which are captured externally. Veins are internal, thus this characteristic makes the systems highly secure, and they are not been affected by the situation of the outer skin. Table 1 shows the characteristic comparison of different types of biometric;

Table!. Characteristic Comparison of Biometric r11

The vein technology has advantage over other biometrics. It has following characteristics [4];

• Universality and uniqueness. The vein images of most people cannot change at all with increasing of ages; different people have different vein images.

• The vein is in body. The finger vein recognition is non-contact identification. Therefore, it does not affect body health and does not make people conflicting feeling.

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• The vein feature is difficult to be forged and changed by surgery.

• The epidermis status doesn't have any effects on the vein recognition almost.

• The equipment has low cost and the collecting course is simple and fast. Therefore, the recognition technology on vein features emerges as the times require and becomes a present developing direction of biometrics.

In this paper, all requirements and specification of the finger vein authentication system and the image processing stages needed for the system are explained. This paper

selected Back Propagation Neural Network training because it provides precious and better results and causes its output to be as close as possible to the expected output.

II. DESCRIPTION OF THE FINGER VEIN AUTHENTICATION

SYSTEM

Most finger vein authentication systems are designed as figurel. Figurel shows authentication system by using neural network (NN), although it can use different pattern matching techniques. The paper explains all the stages in figure 1 , but other papers explained just some of them.

Figure!. Proposed system for finger vein authentication with NN [5][6]

As shown in figurel, the Near-infrared rays generated 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) thus appear as dark areas in an image taken by a CCD camera (Charge-coupled device) located on the opposite side of the finger. Image processing steps and also NN training is done for verifYing the patterns.

A. Image Acquisition

Capturing the image can be done with two methods; 1) infrared-sensitive digital camera with wavelength between 700nm to IOOOnm and banks of LEDs 2) the digital camera with CCD sensor and IR filter which is located on the camera with wavelength 700nm to IOOOnm and banks ofLEDs can be used. Since the infrared-sensitive digital cameras are few, the second method is proposed.

As figure 1, after taking the image of finger vein, normalization, vein extraction and NN training for verifYing pattern are done.

LED control, controls the brightness of the light source to eliminate error caused by individual variations or environmental fluctuations.

B. Pre-processing

Since the captured vein image is dull and includes much noise, therefore it needs to be improved by enhancement functions such as noise removal, illumination dispelling and normalization. However before enhancing, they have to be cropped to eliminate redundant parts.

The location and angle of the finger in the image require some form of normalization, since these qualities will vary

each time. Two-dimensional normalization is done using the outline of the finger on the assumption that the three dimensional location and angle of the finger are constant [7].

C. Vein pattern segmentation andfeature extraction

Image segmentation is one of the most important steps leading to the analysis of processed image data. This process separates the vein pattern from the image background by using different methods such as thresholding, edge based, region based and template matching.

Thresholding is the operation of converting a grayscale image into a binary image [8]. Thresholding is a widely applied preprocessing step for image segmentation, Often the burden of segmentation is on the threshold operation, so that a

properly thresholded image leads to better segmentation [8], Image thresholding has intuitive properties and is simple to

implement [6].

There are mainly two types of thresholding techniques available: global and local. In the global thresholding technique a grayscale image is converted into a binary image based on an image intensity value called global threshold. All pixels having values greater than the global threshold values are marked as 1 and the remaining pixels are marked as O. In local thresholding technique, typically a threshold surface is constructed that is a function on the image domain, and then

the image is thresholded with this threshold surface [8]. Often, due to the variability in the gray level intensities and because of noise, the global thresholding does not work satisfactorily. Therefore, it typically resorts to locally adaptive ones. There are plenty of methods out there that build this locally adaptive threshold surface.

Locally adaptive thresholding is done by calculating

threshold at each pixel. As Figure 2, the image was produced by the adaptive threshold method has an ideal vein result compare with the original one [9]. However, it still contains some burrs and the borderline is not smooth enough. Therefore, it deploys median filter to smooth the image and the result is as Figure 2 (median filtered image)[9].

Conventional methods for extracting line-shaped features from images include the matched-filter method [10],

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mathematical morphology [11], connection of emphasized edge lines [12], and ridge line following for minutiae detection

in grayscale fingerprint images [13]. This process is essential for reliable authentication so as to control the variation of image data caused by body metabolism or changes in imaging

conditions. In particular, uneven brightness due to individual variations in finger size or lighting conditions often appears in the vein pattern image. The system must extract only the vein patterns from such an otherwise unstable image. As shown in Figure2.

Figure2. 2a) Original image, 2b) adaptive threshold image, 2c) median filtered image, 2d) final vein extracted [9]

Since the pattern must be defined as feature descriptor or feature vector, a lot of algorithms have been proposed to pattern definition in order to facilitate the verification step such as Scale-invariant feature transform algorithm [14][15]. This feature vector is the input of neural network.

D. NN training

The last step in the system of authentication is NN training. Neural network is used to train and memorize the identification parameters. The BPN is one of the most well known types of neural network, many different models of the BPN are proposed such as the sum-of- product network and the hybrid sum-of-product network [16]. The architecture of the BPN is as Figure 3.

Figure3. The basic architecture of BPN [16]

It is basically composed of the input layer, hidden layer and output layer. The processing units between the layers are fully

connected, and the input value from each unit is the sum of the previous layer's output values multiplied by a weight vector.

BPN must be trained with a set of training pattern. The training pattern must include both the known input and the expected output, respectively, in the input layer and output layer. Training provides the network parameters and weight values. The values of these weights are modified by the

training patterns. When the weight values are calculated, the expected output could be produced by entering the known input values. This is the basic theorem of the BPN, and the scheme follows the same route. As the BPN is capable of

recalling and identifYing user information, it can be used to

identifY the validity of a user [16].

In finger vein authentication system, the input of NN is a feature vector and the output is the result of verification. A user authentication scheme can be divided into three phases: the user registration phase, user login phase, and user authentication phase [17] [18].

In user registration phase, the feature vectors of finger vein for permissible people are registered in the system by system administrator. The system administrator collects all feature vectors for finger vein as the training set for training the BPN.

In login phase, when a user wants to use the system, he/she only needs to put hislher finger on the designed system to capturing hislher finger vein image.

In the user authentication phase, the system would validate the legitimacy of the user.

Therefore, the output identifies the acceptation or rejection of the user.

E. Testing

The performance of a biometric system is based on measures such as false rejection rate, false acceptance rate, crossover rate, verification time and failure to enroll rate. Following is a brief description of these performance measures [19].

• False rejection rate (FRR) , also commonly referred to as a type I error, measures the percentage of times an individual who should be positively accepted is rejected-in other words, how many times the person cannot gain access.

• False acceptance rate (FAR), also commonly referred to as a type II error, measures the percentage of times an individual who should be rejected is positively matched by the biometric system-how many times the person beat the system.

• Crossover rate, also referred to as the equal error rate (EER), is the point on a graph where the lines representing the FAR and FRR intersect. A lower crossover rate indicates a system with a good level of sensitivity and generally means the system will perform well.

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•

•

Verification time is the average time taken for the actual matching process to occur.

Failure to enroll rate (FTER) is used to determine the rate of failed enrollment attempts.

III. CONCLUSION

This paper reviewed a personal identification system based on patterns of finger veins. since finger veins are inside the body, it is impossible to be stolen and forgotten, and veins are unique in each person, therefore it is known as higher security and easy to use biometric. Moreover, the system use back propagation neural network to higher accuracy and close matching with an expected output. Therefore, this system can be a good candidate for many applications of authentication such as door access control and e-banking operations.

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