A COST-EFFECTIVE IRIS RECOGNITION SYSTEM

USING LINEAR DISCRIMINANT ANALYSIS

AND CROSS-CORRELATION TECHNIQUES

Dr. A K Ramani

Department of computer Science, DAVV, Indore, MP, India

Prof. Sanjay Silakari

Department of computer Science, SATI, Vidisha, MP, India

Pinaki A Ghosh

Department of computer Science, SATI, Vidisha, MP, India

Keywords: biometrics, iris r

ecognition, user authentication, linear discriminant analysis, discrimination.

Abstract: Authorization and identification has become a vital part of security systems of any society. With the

changing of technology implementations in the present scenario, every country specially developing

countries like India needs a cost-effective and reliable solution for authentication system. In this paper,

efficient technique for iris recognition system is described which provides a reliable authentication at low

cost. The proposed system uses linear discriminant analysis and cross correlation methods for identification

and verification purpose. The system was implemented and tested using a dataset of 80 samples of iris with

different contrast quality. The classification rate compared with the well-known methods is also discussed.

1 INTRODUCTION

Biometric is automated methods of identifying a

person or verifying the identity of a person based on

a physiological or behavioral characteristic (Eric J.

Lerner, Feb 2000)(K. L. Kroeker, 2002)(S. Liu et al.,

2002). Examples of physiological characteristics are

fingerprint, hand geometry, facial characteristics and

iris recognition. Behavioral characteristics include

traits that are learned or acquired. Dynamic

signature verification, speaker verification etc. are

examples of behavioral characteristics.

In any pattern recognition system, the main issue

is th

e relation between inter-class and intra-class

variability. For example in face recognition the

difficulty is that the face is changeable organ. It

displays a number of expressions and it varies with

viewing angle, pose and age. it has been proved that

for facial recognition the images taken a year apart,

the best algorithms have error rates of 43% (Phillips

2000) to 50% (Pentland 2000). The inter-class

variability is limited because different faces possess

the same basic set of features. For these reasons iris

patterns become a good alternative approach to

reliable visual recognition (K. L. Kroeker, 2002) of

persons. The iris has the great mathematical

advantage that its pattern variability among different

persons is enormous. As an internal organ of the eye,

the iris is well protected from environment and

stable over time. The iris begins to form in the third

month of gestation and the structure creating its

pattern are complete by the eighth month. Its

complex pattern can contain many distinctive

features such as arching

ligaments, ridges, furrows,

crypts, rings, corona, freckles and a zigzag

collarette. The density of melanin pigment

determines Iris color. Blue irises resulting from an

absence of pigment. The properties of the iris that

enhance its suitability for use in biometric

identification include:

 Protecte

d from the external environment

 Im

possibility of surgically modifying without

the risk of vision

140

Silakari S., K. Ramani A. and A. Ghosh P. (2004).

A COST-EFFECTIVE IRIS RECOGNITION SYSTEM USING LINEAR DISCRIMINANT ANALYSIS AND CROSS-CORRELATION TECHNIQUES.

In Proceedings of the First International Conference on E-Business and Telecommunication Networks, pages 140-145

DOI: 10.5220/0001387001400145

 SciTePress

 Physiological response to light

 Ease of registering its image at some distance

2 IRIS FEATURE AND PROCESS

There are verities of features that can be used to

distinguish one iris from another. One of the main

characteristics is the trabecular meshwork, a tissue

which gives the appearance of dividing the iris in a

radial fashion that is permanently formed by the

eighth month of gestation. During the development

of iris there is no genetic influence on it, a process

known as chaotic morphogenesis that occurs during

the seventh month of gestation, for which identical

twins have four different irises. The iris is protected

behind the eyelid and cornea unlike other biometrics

such as fingerprints or face, the chances of damage

is minimal or nil. The iris is not a subject to be

change with age, it means its pattern remain stable

till death. Generally, the process of iris recognition

system includes the following steps:

 Image Acquisition

 Iris Localization

 Image Optimization

 Comparison

The image of iris can be captured using a

standard camera using both visible and infrared light

and may be either a manual or automated procedure.

In manual procedure, the user needs to adjust the

camera to get the iris in focus and needs proper user

training to be successful. The automated process

uses a set of cameras that locate the face and iris

automatically. Once the camera has located the eye,

the image is thenanalyzed to identify the outer

boundary of the iris, the pupillary boundary and the

center of pupil. This information is used to produce

a vector record called iriscode. This record is then

stored into a database for future comparison.

3 RELATED WORK

Daugman’s work: the visible texture of a person’s

iris in a real-time video image is encoded into a

compact sequence of multi-scale quadrature 2-D

Gabor wavelet coefficients, whose most-significant

bits comprise a 256-byte iris code (J. Daugman,

“How Iris Recognition Works”)(J. Daugman, Nov

1993)(J. Daugman, Dec 2001).

Wildes’ work: is very similar to the above-

mentioned method. A laplacian pyramid is used to

apply a 2-D transformation. A match value is

calculated for the four bands through spatial

correlation (R. Wildes, Sept 1997).

Boles’ work: based on calculating the zero-

crossings representations of the wavelet transform.

These representations are stored as templates and are

used for the matching algorithm (W. Boles, 1997).

4 THE PROPOSED SYSTEM

This system is divided into these parts:

A. Image Acquisition: This is the stage of acquiring

the eye image using digital camera.

B. Iris Localization: Finding the boundary between

the pupil and the iris, the outer boundary of iris and

the center of the pupil.

C. Polar Transformation: Using the center and the

radius we find a polar coordinate system. In this

system the feature of the iris is extracted.

D. Identification: Using linear discriminant analysis

we found a match for the acquired iris feature.

E. Verification: Using cross correlation method it

verifies the identified image to get efficient search

result.

A. Image Acquisition

The iris is a relatively small (1 cm diameter),

dark object and that human operator are very

sensitive about their eyes, this matter requires

careful engineering. The following points should be

concern:

 Describe to acquire images of the iris with

sufficient resolution and sharpness to support

recognition

 It is important to have good contrast in the

interior iris pattern without resorting to a level of

Illumination that annoys the operator

 The image should be well framed (i.e. centered)

 Noises in the acquired images should be

eliminated as much as possible

A COST-EFFECTIVE IRIS RECOGNITION SYSTEM USING LINEAR DISCRIMINANT ANALYSIS AND

CROSS-CORRELATION TECHNIQUES

141

B. Iris Localization

Since the value of the pixels in the pupil not

always be zero so we need an edge detection

algorithm (M. Turhan, Apr 1999) to make all values

of the pupil to be zero to easy determination of the

pupil center and then get the pupil boundary. Get the

center of the pupil by counting the number of black

pixels of each column and row. Then get each row

and column that has maximum number of pixels.

Then determine the center as:

Get the position of first and last pixels (X

a, Ya) and

b, Yb) of this row. Find the center X0 = (Xa + Xb) /

2. Similarly apply same step to find Y

0 = (Ya + Yb) /

2. Consequently, the radius of the virtual circle of

the pupil can be determined. A similar procedure is

extended to locate the outer boundary that can be

apparent by using the mid-point algorithms of circle.

C. Polar Transformation

The image should be transformed into polar

coordinate system. The q(q Î[0;2p]) parameter and

dimensionless p ( p Î[0;1]) parameter describe the

polar coordinate system. Thus the following

equations implement:

I(x(p, q), y(p, q)) à I(p, q)

Where,

x(p, q) = (1-p)*x

p(q) + p*xi(q)

y(p, q) = (1-p)*y

p(q) + p*yi(q)

p(q) = xp0(q) + rp*cosq

p(q) = yp0(q) + rp*sinq

i(q) = xi0(q) + ri*cosq

i(q) = yi0(q) + ri*sinq

Where r

p and ri are respectively the radius of the

pupil and the iris. Figure-4 shows how iris image is

converted to polar coordinates.

iris region

unwrapped image after transformation

D. Identification

Linear Discriminant Analysis (LDA), is perhaps

the best known technique for classifying an

observation of unknown class membership into one

of two populations on the basis of predictors. It

happens frequently in literatures that “classification”

and “discrimination” are used interchangeably,

though the former is used sometimes when the

problem is one of “clustering” (P. N. Belhumeur et

al, Jul 1997)(J. Buckheit et al.).

Given C groups or classes {X

1, X2, ... , Xc} contains

N sample images {x

1, x2, ... , xn} such that each

image belongs to exactly one of the C classes. If the

total scatter matrix S

T is defined as:

ST = ∑ (Xk - µ)(Xk - µ)T

k-1

Where n is the number of sample images, and m Î Rn

is the mean image of all samples, then after applying

the linear transformation W

T, the scatter of the

transformed feature vectors {y

1, y2, ... , yn} is

TSTW. In PCA, the projection Wopt is chosen to

maximize the determinant of the total scatter matrix

of the projected samples, i.e.,

opt = arg max ⎜WTSTW ⎜

= [W

1 W2 ... Wm]

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Where {Wi ⎜i = 1, 2, ..., m} is the set of n-

dimensional eigen-vectors of S

T corresponding to the

m largest eigenvalues.

Linear Discriminant Analysis (LDA) is an

example of a class specific method, in the sense that

it tries to “shape” the scatter in order to make it more

reliable for classification. This method selects W in

such a way that the ratio of the between-class scatter

and the within-class scatter is maximized. Let the

between-class scatter matrix be defined as

SB = ∑ Ni (µi - µ)(µi - µ)T

i=1

and the within-class scatter matrix be defined as

SW = ∑ ∑ N i(Xk - µi)(Xk - µi)T

i=1 Xk∈Xi

Where m

i is the mean image of class Xi , and Ni is the

number of samples in class X

i. If SW is nonsingular,

the optimal projection Wopt is chosen as the matrix

with orthonormal columns, which maximizes the

ratio of the determinant of the between-class scatter

matrix of the projected samples to the determinant of

the within-class scatter matrix of the projected

samples, i.e.,

⎜W

TSBW ⎜

opt = arg max ⎯⎯⎯⎯⎯

⎜W

TSWW ⎜

where {W

i ⎜i =1, 2, ... , m} is the set of generalized

eigen-vectors of S

B and SW corresponding to the m

largest generalized eigenvalues {l

i ⎜i = 1, 2, ... , m}

i.e., S

BWi = λiSWWi i = 1, 2, ... , m

Note that there are at most C - 1 nonzero generalized

eigen-values, and so an upper bound on m is C - 1,

where C is the number of classes. The solution is

given by the min(C - 1) eigen-vectors (called

“canonical variates” by Rao) of S

W—1SB.

E. Verification

Once the iris was identified the system verifies

it using cross correlation technique to produce better

search results.

Given two images 1 and 2, image-1 is the template

image and image-2 is the image to be searched

∑ ∑ (g

1(x, y) - µ1) (g2(x, y) - µ2)

x y

ρ = ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯

√ ∑ ∑ (g

1(x, y) - µ1)2 ∑ ∑ (g2(x, y) - µ2)2

x y x y

-1 ≤ ρ ≤ 1

where,

1(x, y) = individual gray values of template matrix

1 = average gray value of template matrix

2(x, y) = individual gray values of search matrix

µ2 = average gray value of search matrix

5 RESULTS

To show the effectiveness of the proposed system, in

our implementation, we used 10 classes each contain

5-9 iris images acquired under different conditions.

The images were collected as gray-scale images. For

each iris class, we randomly choose four samples for

training and the rest for testing. In identification

tests, an average correct classification rate of

98.97% is achieved. These tests were performed on

300 MHz uniprocessor Pentium based system.

Operating states of our previous system.

Operating states of system using LDA.

Operating states of the proposed system.

Comparison with Existing Methods

Daugman documented first iris recognition

system in 1993 (J. Daugman, Nov 1993). This

system is based on wavelet transformation. It is

implemented on a RISC general-purpose CPU and

32X32 array of 74F86 ICs. Wildes described a

system for personal verification based on automatic

iris recognition in 1996 (R. Wildes, Sept 1997). This

system used a very similar technique l i k e

Daugman’s. It was implemented on Sun

SPARCstation 20 and written in UNIX C Shell

languages without optimization. Boles proposed a

system using zero-crossing representation (W.

Boles, 1997). Our early works was based on

correlation method and PCA. All these algorithms

A COST-EFFECTIVE IRIS RECOGNITION SYSTEM USING LINEAR DISCRIMINANT ANALYSIS AND

CROSS-CORRELATION TECHNIQUES

143

are based on gray image. A gray iris image can

provide enough information to identify different

individuals. The methods proposed by Daugman and

Wildes are the best among all, but these methods

require expensive hardware like RISC & parallel

processing architecture systems to implement it. The

proposed system can run on uniprocessor system. So

we can say that this proposed system is very much

cost-effective between these methods.

6 CONCLUSION

In this paper, we have presented a new and effective

algorithm for iris recognition. The proposed system

uses linear discriminant analysis for identification

then verifies the result using cross-correlation.

Experimental results have shown that the proposed

algorithm achieves

high performance at low cost. Developing

countries like India where population density and

crime rates are increasing day by day, such high

technology oriented authentication systems are

desperately in requirement. The existing iris

recognition systems require high cost computing

system, which is not feasible for developing

countries. The proposed system is highly suitable for

such countries and can be used for a wide range of

application areas involving forensic science, crime

search, driver’s license, passport, etc.

Some efficient techniques are used in this

proposed method, these are:

 A computer graphics algorithm for detecting the

center of the pupil and localizing the iris area.

 Transforming the localized iris area into a

simple coordinate system.

 Identification process based on LDA.

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CROSS-CORRELATION TECHNIQUES

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