Contemporary Problems of Social Work


Neural networks apparatus in biometrics

Автор/Author: Ziroyan M.A., Tusova E.A., Hovakimian A.S., Sargsyan S.G.

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Список литературы/References:

1. Kucharev G.A. Biometrical systems: methods and means of identification. St.Petersbourg


2. Degtarev A. Methods of identification by iris. Computer graphics and multimedia. 2004. Issue

№ 2 (6)

3. Matveev I. Human identification by iris. Security systems– 2004. Issue №5.


5. Anil K. Jain, Jianchang Mao, K. M. Mohiuddin. Artificial Neural Networks: A Tutorial. Computer

– Special issue: neural computing: companion issue to Spring 1996 IEEE Computational

Science & Engineering, Volume 29, Issue 3, March 1996.

6. Samta Gupta, Susmita Ghosh Mazumda. Sobel Edge Detection Algorithm. International

Journal of Computer Science and Management Research. Vol 2, Issue 2, February 2013

7. Just Kjeldgaard Pedersen, Simon. Circular Hough Transform. Aalborg University, Vision,

Graphics, and Interactive Systems. November, 2007.

8. Jolliffe, I.T. Principal Component Analysis (Springer Series in Statistics), Springer; 2nd

edition (October 2, 2002)

9. Anna Hovakimyan , Siranush Sargsyan , Arshak Nazaryan. Self-Organizing Map Application for

Iris Recognition. Journal of Commun. & Comput. Eng. ISSN 2090-623,,

Volume 3, Issue 2, 2013, pp 10-13

References in Roman script:

1. Kuharev, G.A. Biometricheskie sistemy: metody i sredstva identifikatsii lichnosti cheloveka.

SPb., 2001

2. Degtyareva A. Metody identifikatsii lichnosti po raduzhnoy obolochke glaza. Komp’yuternaya

grafika i mul’timedia2004. – Vyp. № 2 (6)


3. Matveev, I. Raspoznavanie cheloveka po raduzhke . Sistemy bezopasnosti. – 2004.Vyp. №5.


5. Anil K. Dzheyn, Jianchang Mao, K. M. Mohiuddin. Iskusstvennye Neyronnye Seti: Uchebnoe

Posobie. Komp’yuter – Spetsial’nyy vypusk: neyronnye vychisleniya: kompan’on vopros do

vesny 1996 po standartu IEEE vychislitel’nyh nauk i inzhenerii, tom 29, Vypusk 3, mart 1996.

6. Samta Gupta, Nepal’skaya Gosh Mazumda. Sobel Algoritm Obnaruzheniya Kraya.

Mezhdunarodnyy zhurnal vychislitel’noy tehniki i menedzhmenta issledovaniy. Tom 2,

Vypusk 2, Fevral’ 2013

7. Prosto Kjeldgaard Pedersen, Saymon. Krugovoy Hafa. Ol’borgskogo universiteta, videnie,

grafiki i interaktivnyh sistem. Noyabr’, 2007.

8. Jolliffe, i. t. Analiz glavnyh komponent (Springer Seriya v statistike), Springer; 2-e izdanie (2

oktyabrya 2002)

9. Anna Ovakimyan Siranush Sarkisyan , Arshak Nazaryan. Samoorganizuyushayasya karta dlya

identifikatsii po raduzhnoy obolochke glaza. Vestnik kommun. & Komp’yut. Angl. ISSN v

2090-623,, tom 3, Vypusk 2, 2013, str. 10-13

Содержание статьи/Article:

Over the past two decades, biometric identification technologies have made a great step

forward and at present take a strong position in the security market, which are in great demand

by various institutions and organizations: banks, airports, libraries, and others. In biometric

identification system the identification is carried out of biometric parameters of a person and

not by a clue or card. According to experts, the share of biometric systems in the coming years

will become a significant part of the total security market [1],

Biometric identification methods divert. They are based on static biometric characteristics

of humans, such as fingerprints, facial geometry, retina, iris, hand vein pattern, as well as the

dynamic characteristics, such as voice, handwriting, heart rate, gait. According to most experts,

the main biometric characteristics that continue to be used for identification, fingerprints, face

geometry and iris [2,3].

The iris is a unique characteristic of man: it does not change throughout his life. It is believed

that the method of identification by the iris is the most accurate biometric technologies [1,2,3].

But today in the international market this method is used by only 6.9%, while the technology of

biometric fingerprint recognition forms more than half of the market.

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Currently, the information technologies are of great help to achieve the objectives of

biometric identification ensuring efficient and effective processing of biometric information.

Biometric identification problem can be related to the recognition pattern, among which a

special place occupies the problem of classification. In these problems, the classification under

way to understand its relevance to one of the previously known classes of objects, formed on

the basis of data on the master image.

There are many ways to solve the problems of classification of images, among which we can

note the technology of artificial neural networks [4].

The paper considers the problem of constructing and implementing a software system that

takes as input a picture of the human eye, performs pre-processing of the input data on the

allocation of the iris using Sobel operator and Huff transformation and represents the image of

the iris as a binary vector. Before we apply the input vector to the neural network classification

the system selects the essential components in the vector, thereby reducing its dimension. The

designed neural network belongs to the type of self-organizing maps, coached by the method of

“learning without a teacher” and recognized as enough satisfactory for solving image pattern

classification [5, 6, 7]. Software implementation of the system has been implemented in Java.

The system has been tested on a number of test cases for the purpose of the experimental setup

used by the neural network.

1. Neural networks and self-organizing maps

Artificial neural network (ANN) is a mathematical model of the biological neuron. It consists

of neurons relating to each other. A neural network is subjected to learning by providing

her input information in the form of numerical sequences. In the training process internal

connections between neurons are set up through which the network endows the ability to

recognize unfamiliar images.

There are different types of neural networks that differ in both the topology and the learning

algorithms. In classification problems, some of the images are the most effective self-organizing

maps (SOC), which are trained to algorithms unsupervised [4]. SOC consists of two layers: the

input and functional. In the functional layer the neurons are located on the grid composed of

cells. Each neuron takes a single cell and is connected to all neurons of the input layer (Figure


Fig.1. The structure of the self-organizing map

SOC operates on the principle of “winner takes all”. That is, when a sequence of input vectors

are applied to the network, all the neurons of the functional layer cause activation function,

which reflects the relationship between the input data and the weights of these neurons.

The winner becomes the neuron for which the activation function receives the optimal value,

say maximum or minimum. In this case, as the activation function is selected the Euclidean

distance between the input data and the weight of neuron function where an optimum value

is considered its minimal value. If V_1, V_2, V_3, ..., V_n is the input vector, and the functional

layer SOC consists of k neurons with weighting coefficient

W_11, W_12, W_13, ..., W_1n

W_21, W_22, W_23, ..., W_2n


W_k1, W_k2, W_k3, ..., W_kn

where W ij weighting coefficient of i-th neuron of functional layer associated with the j-th

neuron of the input layer, then the winner will be the neuron for which

At the stage of learning the network some training sequence of data is presented. The essence

of teaching is that at each step the neuron-winner is determined, and the neurons, located in a

neighborhood of the neuron-winner move closer to him. As a result of training neurons closely

located collect in the grid area, which are determined by the training sequence (Figure 2).

Fig.2. Neighborhood of the neuron-winner

The radius of the neighborhood of the neuron-winner varies according to the formula:

Experiments show that as the initial neighborhood radius value it is appropriate to take

where the width is the width of the functional layer mesh, height, λ is time constant

calculated by the formula

where n is the number of training iterations.

Thus, the radius of the neighborhood is changing exponentially (Figure 3)

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Fig 3. Changing the radius of neighborhood of the neuron-winner

During the training, a network of neurons and neurons winner of its σ- neighborhood change

their weights as follows:

W (t + 1) = W (t) + θ (t) L (t) (V (t) -W (t))

where t is the number of iteration, L (t) = L_0 exp (-t / λ), t = 1,2,3, ...,

θ (t) = exp⁡ (- 〖〗 dist ^ 2 / (2σ ^ 2 (t))), t = 1,2,3, ..., dist –is the distance between neuron

and neuron-winner on the grid of the functional layer. The function L (t) is called the learning

rate, θ (t) reflects the influence of the location of the neuron on the grid. It is obvious that

most of the weight change of the winning neuron (Fig. 4).

Fig. 4. Effect of the distance from the neuron-winner at changing the balance

After training, the network is able to recognize the data, similar to those on which it is


2. Recognition of the iris

Iris recognition is the task of biometric identification. The iris is unique to each person and

remains unchanged throughout his life. It is reliable characteristics of identification for it can’t

be a subject to distortions caused by time or by direct contact with the capture of the sample.

Currently, there are many software systems that recognize human by iris [2,3].

In this paper the problem is solved by using a neural network self-organizing map type.

The problem of iris pattern recognition is divided into three subtasks:

– detection of the circle framing the iris on the eye,

– allocation of bandwidth to the iris and its pretreatment in order to isolate the iris essential

characteristic information

– iris recognition using a neural network type of self-organizing maps.

There are various algorithms to detect contours and shapes in the image. The most effective

algorithms among these are based on the transformation of Huff (Huff Transformation) [5]. In

the current task considered, we apply to Huff transformation circle as far as the desired images

in the image are the circles; one that limits the iris, and the other that limits the pupil.

Huff transformation is performed more effectively when algorithms for detection and

isolation of the desired shapes on the borders of the image are previously applied. In this case

we used Sobel operator to solve this problem, [6]. This is a discrete differential operator that

computes the approximate value of the gradient function, reflecting the brightness of the

image at each point. Thus the magnitude and direction of increasing the brightness in each

pixel of the image is detected, and hence marking the probability that the pixel is on a border of

the image. Fig. 5 shows the result of the Sobel operator applied to the image of the human eye.

The figure shows the selected borders of the eye.

Fig. 5. The result of the Sobel operator

You can improve the efficiency of the Sobel operator, considering the fact that the iris

region is surrounded by white, and it itself is of darker color. Consequently, there is a threshold

luminance pixel value, where the pixels with greater brightness value lie outside the region of

the iris, and pixels with lower luminance value in the iris area. Thus, if the pixel value is less

than the threshold, it is replaced with zero, otherwise it is replaced with the maximum possible

value. As a result of such replacement the image will be split into two classes of pixels - dark

and light, converting the image from color to black and white and the increasing contrast and

brightness of the pixels, owing to which Sobel operator will work more efficiently.

Huff transformation is a method of finding the pieces on the image. This is a common

mechanism by which you can find all kinds of shapes known in advance: straight lines, ovals, circles,

etc. In this paper we apply the transformation krugovoya Huff (Circular Huff Transformation),

designed to detect circles in the image that has been processed by Sobel operator. The data is

stored in a three-dimensional matrix, where the first two dimensions define two-dimensional

image, and the third dimension should take the value of the radius, where the circle is sought

in the image [6]. Through all the boundary points, i.e. the points where the pixel value is

not zero, the circles are conducted, and the values in the matrix for the appropriate pixels are

incremented by one. As a result of the algorithm the point at which the circles intersect will be

the center of the desired circle. We take the circle, the radius of which is the maximum in the

matrix. In practice, usually circles with a fixed radius in advance are looked for .

After detection of circles, limiting the iris and the pupil, the band is allocated between

the circles,which contains information only on the iris. This information is represented as

n-dimensional vector where n is sufficiently large. The aim is to transform the vector in order to

isolate the main components that contain the most important information about the subject of

pattern recognition. We have applied the method of principal component analysis, the essence

of which is to identify similarities and differences in the values of attributes characterizing the

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set of study data [7]. The method works with a certain set of the objects represented by vectors

of n-dimensional space. By analysis of covariance matrix and finding its own vectors and own

values, n-dimensional space is replaced by m-dimensional space (m≤n), elements of which

contain the most important information about the object. Thus we can discard appropriately

selected (n - m) components of the input vector, without losing the basic characteristics of the


Vector processed with typical for iris information is input to the neural network,that solves

the problem of recognition through classification. Neural network of software is designed and

implemented in the type of neural network self-organizing map. The isolation of the major

components in the input vector encoding the iris, has also led to a decrease in the input layer of

neurons network that contributes to its effectiveness. The network is trained on a representative

set of images and tested on test cases. Originally selected network topology and the values of

its parameters have been refined as a result of experimental evaluation of the network.

3. Experimental setup parameters of self-organizing map

A neural network for biometric identification on iris has been implemented in Java. A library

of special classes has been developed, which provides parallel processing [8]. The network is

trained on a certain training set of images, and tested on a number of test cases for the purpose

of the experimental setup of its parameters characterizing the topology of the network and the

details of its training.

The self-organizing map has four parameters, the values of which depend on the efficiency

of the network. These parameters are the number of internal neural network, the characteristic

of the grid which expresses the ratio of the width of the grid on which the neurons are allocated,

relation to its height, to the initial learning rate, the number of training iterations. On the basis

of experiments to classify iris the values of network parameters have been found under which

the network works most effectively. Efficiency is defined correctly with sharing images in the

set of test cases. Fig .6-9 demonstrate the results of assessment of network parameters.

Figure 6 shows the dependence of the efficiency of the network on the number of neurons

in the functional layer. The dependence is expressed in a percentage. The number of neurons

is taken of multiple number of training vectors. The abscissa represents the values of the

coefficient of multiplicity, the vertical axis shows the percentage of correctly recognized images.

Figure 6. The dependence of the efficiency of the network by the number of neurons

As the graph shows, network efficiency first increases sharply with increasing frequency

factors. Then the efficiency of the network does not grow. This value limit is equal to the

multiplicity factor 10. In other words, the network is most effective when the number of neurons

is approximately 10 times greater than the amount of training data.

Another parameter is the shape of a rectangle network mesh the functional layer. This

parameter is expressed by the ratio of net height to its width. Figure 7 shows the efficiency of

the network on the shape of a rectangle grid. The abscissa represents the value of the ratio of

height to its width grid, the vertical axis shows the percentage of correctly recognized images

Figure 7. Dependence of the efficiency on the shape of the mesh network

As can be seen from the graph, the network efficiency grows with an increasing net height to

its width, and at a value of 100, it almost stops growing. This value is chosen as the best value

for the parameter network.

Two other network parameters are associated with network training. The first is the initial

learning rate. Figure 8 shows the dependence of the efficiency of the network on this factor.

Figure 8. The dependence of the efficiency of the network from the initial learning rate

As seen on the chart, at first the effectiveness of the network is growing, but after reaching

the value of the learning rate of 0.1, it begins to decrease. Thus, the value of 0.1 for the initial

learning rate is considered optimal for the problem.

The last parameter of the network, which determines its effectiveness - is the number of

iterations training schedule. Fig. 9 illustrates this relationship. The abscissa represents values

of the number of iterations, and the ordinate shows percentage of correctly recognized images.

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Figure 9. The dependence of the efficiency of the network on the number of training iterations

By plotting one can conclude that in the beginning, when the number of iterations increases,

the network efficiency grows. But when it reaches a value of 100, it almost remains unchanged.

We can therefore conclude that the number of iterations as the best can assume a value of 100.

Thus, on the basis of experiments testing the self-organizing maps and analysis of the

parameters under study, it can be stated that the efficiency of neural network like selforganizing

map for the task of recognition of the iris with an appropriate choice of parameter

values can form an average of 95%.

The network was also tested for other classification tasks, and it has been found that the

values of the parameters of the network obtained are also very satisfactory.

The study and practical implementation of a software system to solve the problem of

biometric identification of the iris using neural network technology have shown that the

proposed mechanism can be applied successfully in practice. It is highly desirable that the

network was trained on a larger number of images. This would provide an opportunity to better


It can be asserted with a certain degree of confidence that the proposed mechanism is quite

versatile and can be used to solve a wide class of problems for data mining.

Ключевые слова/Tags1: biometric identification, pattern recognition, neural network, network parameters, network configuration.