features vector for personal identification based on iris texture

FEATURES VECTOR FOR PERSONAL 

IDENTIFICATION BASED ON IRIS TEXTURE 

R. P. Moreno 

Departamento de Engenharia Elétrica 

EESC - USP 

Av. Trabalhador Sãocarlense, 400 

São Carlos / SP – Brasil 

raphael@digmotor.com.br 

A. Gonzaga 

Departamento de Engenharia Elétrica 

EESC - USP 

Av. Trabalhador Sãocarlense, 400 

São Carlos / SP – Brasil 

adilson@sel.eesc.sc.usp.br 

Abstract 

This work presents a biometric method <strong>for</strong> <strong>identification</strong> <strong>vector</strong> building <strong>based</strong> on 

human iris <strong>features</strong>. The proposed work is <strong>based</strong> on iris texture <strong>features</strong> analysis and 

extraction. The work is divided in 3 steps. In the first, the eye image is preprocessed 

and Hough Trans<strong>for</strong>m <strong>for</strong> circles does the iris localization and segmentation. In the 

second step, the iris <strong>features</strong> in<strong>for</strong>mation is extracted by a second order statistical 

approach, using the Haralick’s texture <strong>features</strong> as classification parameters. Finally in 

the last step, the in<strong>for</strong>mation is saved in a feature <strong>vector</strong> that can be used <strong>for</strong> iris 

recognition. 

Keywords: Haralick, pattern recognition, biometrics, iris, texture. 

1 Introduction 

Biometry is the group of automatic methods used in people recognition, <strong>based</strong> in physiological or 

behavioral <strong>features</strong>. Examples of behavioral <strong>features</strong> are signature, gait, voice, etc. Examples of 

physiological <strong>features</strong> are fingerprint, face, iris, hand geometry, the veins in ocular retina, etc. One 

of the biometric advantages, if it is compared with conventional methods, is the possibility of 

identify, authenticate and localize people without requiring that they carry cards or memorize 

passwords [1]. 

Recently, the number of studies and researches in iris recognition has increased significantly. This 

crescent increase happens because, in <strong>identification</strong> systems, iris is more efficient, stable and 

accurate than the others biometric <strong>features</strong> [2]. 

The iris is the circular and retractile membrane, which is localized in the center behind the ocular 

globe. It’s situated between the cornea and the anterior part of crystalline, and it has an orifice, the 

pupil. The fig. 1 shows the iris position in relation to a person eye. 

Fig. 1. Eye anatomy. 

110

R. M. Moreno; A. Gonzaga 

Formed by a multi-layer structure, the iris has a very complex color and shape pattern. It can be 

observed in fig. 2. 

Fig. 2. Iris structure seen in a frontal sector. 

The human iris possibility of been used as a biometric signature was first suggested by 

ophthalmologists [3]. They verified, through clinical experience, that each iris had a very detailed 

texture. 

Recognition biometric systems <strong>based</strong> on iris study are possible because of some <strong>features</strong>. The most 

important of them is the iris uniqueness, which is a result of the chaotic organization of its patterns, 

established by the initial conditions in the embryonic genetic, [4]. The probability of two people 

having the same iris pattern is estimated in one in 10 78 people. As written in [5], the right and left 

eyes of the same person have different texture patterns. 

Another important feature is the iris stability. A normal iris is usually lubricated and preserved by 

the cornea and aqueous humor, becoming one of the most protected organs in a human body. 

Besides, the localization, size, shape and orientation remain stable and fixed from about one year of 

age throughout life [6]. 

2 Iris localization 

The iris localization in an image is the task of find a ring situated between the pupil and the 

sclera. It is equivalent to finding non-concentric circles which determinate the internal and 

external borders of the ring. The method used in this work finds the center coordinate and 

the ray of the pupil, which is the internal border of the iris, through the Hough Trans<strong>for</strong>m 

(HT) <strong>for</strong> circles [7]. 

Compared with all others parts in the image, the pupil is much darker. So, after the application of a 

threshold, followed by an edge detector, the image will be ready to the Hough Trans<strong>for</strong>m 

technique. 

The width of the iris ring used is fixed, separating just the iris region near the pupil. 

Due to partial iris occlusion by the eyelid and eyelashes, the upper part of the iris ring was removed 

and it is not used in the algorithm sequence. The fig. 3 shows an original image (a) and the same 

image after the iris localization (b). 

111


Fig. 3. (a) Original image. (b) Segmented iris. 

After the pupil and consequently the iris localization, the system becomes robust to the pupil size 

and the position of the eye in the image. 

3 Haralick’s <strong>features</strong> 

In this work, it is proposed an iris feature extraction methodology <strong>based</strong> in the Haralick’s approach 

[8]. It uses second order statistics, by analyzing the relative position of the image pixels. Through 

this method, distinct images with equal first order histograms still can be differentiated. 

The second order statistical measures are done in probabilities’ distributions or co-occurrence 

matrixes. These matrixes (GLCM – gray level co-occurrence matrix) are bi-dimensional 

representations showing the spatial occurrence organization of the gray levels in an image. They 

represent a bi-dimensional histogram of the gray levels, where fixed spatial relation separates 

couples of pixels, defining the direction and distance (d,) from a referenced pixel to its neighbor. 

To build these matrixes, the couple of pixels’ variation is done in the following angles: 0°, 45°, 90° 

e 135°, originating four distinct co-occurrence matrixes. 

After computing the co-occurrence matrixes, several second orders statistical calculus can be 

calculated, including the Haralick’s <strong>features</strong>. These are the <strong>features</strong> used in this work: 

 

 

 

 

 

Second Angular Moment (SAM): measures the local homogeneity of gray levels in an 

image. The SAM equation is given by: 

SAM 

n 1 n1 

i 0 

i0 

2 

P 

( i, 

j, 

d , ) 

 

Contrast: it measures the local quantity of gray levels in an image. The Contrast 

equation is given by: 

n 

1n 

 

 

1 2 

Contrast ( i j) 

P( 

i, 

j, 

d, 

) 

(2) 

i 0 i 0 

Entropy: also called as dispersion degree of the gray levels, it measures together with 

the SAM, the homogeneity in an image. The Entropy equation is given by: 

Entropy 

n 

 

1 n1 

i0 

i 

0 

P( 

i, 

j, 

d, 

 

(1) 

) log2 P( 

i, 

j, 

d, 

) 

 

(3) 

Inverse Difference Moment (IDM): The IDM equation is given by: 

n 

 

1 n1 

i0 

i0 

1 

IDM 

P( 

i, 

j, 

d, 

) 

2 

(4) 

1 

( i j) 

Correlation: it represents the linearity dependence of gray levels in an image. The 

Correlation equation is given by: 

112


Where, 

n n 

1 

i j P i j d x 

y 

Correlation 

i 

i 

1 

( , , , ) 

 

0 0 

(5) 

x 

y 

 

n 

1n 

 

 

1 

x i P( 

i, 

j, 

d, 

) 

i 0 i 0 

, n 

1n 

 

 

1 

y j P( 

i, 

j, 

d, 

) 

i 0 i 0 

n 

1n 

 

 

1 2 

n 

x 

i P( 

i, 

j, 

d, 

) x , 1n 

 

i 0 i 0 

 

1 2 

y 

j P( 

i, 

j, 

d, 

) y 

i 0 i 0 

And, x and y represent the mean in X and Y direction, and y and y represent the 

variance. 

4 Feature <strong>vector</strong> 

In this work, the segmented iris is divided into six sectors having the same size, as showed in fig. 4. 

The number of sectors was defined to increase the classifying method efficiency through the 

texture <strong>features</strong>. 

Fig. 4. Segmented iris divided in six sectors. 

For each sector, the five Haralick’s <strong>features</strong> are calculated resulting in a feature <strong>vector</strong> with 30 

values. This <strong>vector</strong> will be saved in the database or used in an <strong>identification</strong> or authentication 

process. 

5 Image database 

The image database used to test the algorithm, CASIA version 1.0 [9], was developed by the Iris 

Recognition Research Group - National Laboratory of Pattern Recognition (NLPR) from the 

Institute of Automation, Chinese Academy of Sciences. The dataset has images with 256 gray 

levels, and resolution of 320x280 pixels, captured through a digital optical sensor also developed 

by the NLPR. There are 756 images of 108 eyes from 80 people. 

In this dataset, seven images were taken from each iris, in two different moments. In the first one, 

three images were taken and in the second moment, one month later, more four images were taken. 

6 Tests and results 

The algorithm finds the proximity of two irises calculating the normalized Euclidian distance of the 

two <strong>features</strong> <strong>vector</strong>s, as described in equation 6. 

( A, 

B) 

30 

i 1 

 

 

2 

A i B i 

Ai 

D (6) 

113


The fig. 5 shows an example of the distance calculus between an iris and the others 107 resting in 

the database. 

Fig. 5. Euclidian distance example. 

For each comparison between two irises' images, the algorithm returns a number. To show if the 

<strong>vector</strong>s are from the same iris, the algorithm compare the value returned with a t (threshold) value, 

previous established. With this in<strong>for</strong>mation, it's possible to evaluate the system accuracy varying 

the t value and building the ROC curve (receiver operating characteristic). 

To build the ROC curve, it was generated a dataset with the mean feature <strong>vector</strong> taken from each 

one of the 108 irises of the database. The mean <strong>vector</strong>s were obtained calculating the means among 

the seven <strong>features</strong> <strong>vector</strong>s from each image of the same iris. 

After that, <strong>for</strong> each t value it was done an authentication try between each database image and the 

others 107 irises. As the database has 756 images, 81648 authentication tries were done. During the 

authentication tries, the number of false accepted (FA) and false rejected (FR) were found. The 

Table 1 and the figure 6 show the FA and FR probability's distribution with the t variation. 

Table 1. False accepted probability, P (FA) and false rejected probability, P (FR). 

t P (FA) P (FR) 

0,00 100,000% 0,000% 

0,05 47,153% 3,571% 

0,10 43,496% 3,704% 

0,15 39,726% 4,101% 

0,20 35,634% 4,762% 

0,25 31,469% 6,085% 

0,30 27,150% 7,011% 

0,35 22,927% 7,804% 

0,40 18,671% 9,392% 

0,45 14,532% 11,243% 

0,50 10,673% 13,889% 

0,55 7,347% 17,989% 

0,60 4,604% 22,354% 

0,65 2,479% 27,910% 

0,70 1,024% 35,185% 

0,75 0,307% 47,222% 

0,80 0,066% 62,963% 

0,85 0,002% 78,704% 

114

0,90 0,000% 94,577% 

0,95 0,000% 100,000% 


Fig. 6. False accept probability, P (FA) and false reject probability, P (FR). 

The ROC curve, which represents the system accuracy, is showed in fig. 7 and was built with the 

P (FA) e P (FR) values showed in figure 6. 

Conclusion 

Fig. 7. ROC curve. 

The ROC curve analysis validates the Haralick’s <strong>features</strong> <strong>for</strong> using as a biometric feature extraction 

of human being, because they can reproduce the iris unique feature. Also, it is possible to conclude 

that the way chosen to divide the iris ring is an efficient method to obtain a uni<strong>for</strong>m texture region. 

Another important point is that in the majority of the cases, the false accepted and the false rejected 

were obtained due to some kind of fail in the iris image. The partial occlusion and the lack of focus 

were the principal fail reasons. Fig. 8 shows an eye image with the iris very obstructed, what turns 

its <strong>identification</strong> a hard job. 

115


Fig. 8. Partial occlusion of the iris. 

The <strong>identification</strong> also becomes difficult when the images, used to build the mean feature <strong>vector</strong>, 

have many differences. 

References 

[1] Negin, M. et al. (2000). An iris biometric system <strong>for</strong> public and <strong>personal</strong> use. IEEE, p.70-75. 

[2] Jain, A.K. et al. (1999). Personal Identification in a network society. Norwell, MA - Kluwer. 

[3] Adler, F.H. (1965). Physiology of the eye: Clinical application. The C. V. Mosby Company, 4a 

edição, Londres. 

[4] Daugman, J. (1993). High confidence visual recognition of persons by a test of statistical 

independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15, no. 

11, p.1148-1161. 

[5] El-Balkry, H.M. (2001). Human iris detection using fast cooperative modular neural nets. 

Neural works, Proceedings of International Joint Conference on IJCNN’01, v.1, p.577-582. 

[6] Willians, G.O. (1997). Iris recognition technology. IEEE AES Systems Magazine, abril 1997, 

p.23-29. 

[7] Haralick, R.M.; Shanmugan, M.K. (1973). Computer classification of reservoir sandstones. 

IEEE Transactions on Geoscience Electronics, v.11, no. 4, p.171-177, Oct. 

[8] Hough, P.V.C. (1962). Methods and means <strong>for</strong> recognizing complex patterns. U.S. Patent 

3.069.654. 

[9] CASIA. Iris Image Database versão 1.0, http://www.sinobiometrics.com. 

116

features vector for personal identification based on iris texture

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?