1

Improving Iris Recognition Performance using

Segmentation, Quality Enhancement, Match Score

Fusion and IndexingMayank Vatsa, Richa Singh, and Afzel Noore

Abstract—This paper proposes algorithms for iris segmenta-tion, quality enhancement, match score fusion, and indexing to

improve both the accuracy and speed of iris recognition. A curve

evolution approach is proposed to effectively segment a non-idealiris image using the modified Mumford-Shah functional. Different

enhancement algorithms are concurrently applied on the seg-mented iris image to produce multiple enhanced versions of iris

image. A SVM based learning algorithm selects locally enhancedregions from each globally enhanced image and combines these

good quality regions to create a single high quality iris image.Two distinct features are extracted from the high quality iris

image. The global textural feature is extracted using 1D log polarGabor transform and the local topological feature is extracted

using Euler numbers. An intelligent fusion algorithm combinesthe textural and topological matching scores to further improve

the iris recognition performance and reduce the false rejectionrate, while an indexing algorithm enables fast and accurate iris

identification. The verification and identification performance ofthe proposed algorithms are validated and compared with other

algorithms using CASIA Version 3, ICE 2005, and UBIRIS irisdatabases.

Index Terms—Iris Recognition, Mumford-Shah Curve Evolu-

tion, Quality Enhancement, Information Fusion, Support VectorMachine, Iris Indexing.

I. INTRODUCTION

CURRENT iris recognition systems claim to perform

with very high accuracy. However, these iris images are

captured in a controlled environment to ensure high quality.

Daugman proposed an iris recognition system representing iris

as a mathematical function [1]-[4]. Wildes [5], Boles [6], and

several other researchers proposed different recognition algo-

rithms [7]-[32]. With a sophisticated iris capture setup, users

are required to look into the camera from a fixed distance and

the image is captured. Iris images captured in an uncontrolled

environment produce non-ideal iris images with varying image

quality. If the eyes are not opened properly, certain regions of

the iris cannot be captured due to occlusion which further

affects the process of segmentation and consequently the

recognition performance. Images may also suffer from motion

blur, camera diffusion, presence of eyelids and eyelashes, head

rotation, gaze direction, camera angle, reflections, contrast,

luminosity, and problems due to contraction and dilation. Fig.

1 from the UBIRIS database [26], [27] shows images with

some of the above mentioned problems. These artifacts in iris

M. Vatsa, R. Singh, A. Noore are with Lane Department of ComputerScience and Electrical Engineering, West Virginia University, Morgantown,

WV, USA mayankv, richas, noore@csee.wvu.edu.

images increase the false rejection rate (FRR) thus decreasing

the performance of recognition system. Experimental results

from the Iris Challenge Evaluation (ICE) 2005 and ICE 2006

[30], [31] also show that most of the recognition algorithms

have high FRR. Table I compares existing iris recognition

algorithms with respect to image quality, segmentation, en-

hancement, feature extraction, and matching techniques. A

detailed literature survey of iris recognition algorithms can

be found in [28].

This research effort focuses on reducing the false rejection

by accurate iris detection, quality enhancement, fusion of

textural and topological iris features, and iris indexing. For

iris detection, some researchers assume that iris is circular or

elliptical. In non-ideal images such as off-angle iris images,

motion blur and noisy images, this assumption is not valid

because the iris appears to be non-circular and non-elliptical.

In this research, we propose a two-level hierarchical iris

segmentation algorithm to accurately and efficiently detect iris

boundaries from non-ideal iris images. The first level of iris

segmentation algorithm uses intensity thresholding to detect

approximate elliptical boundary and the second level applies

Mumford Shah functional to obtain the accurate iris boundary.

We next describe a novel Support Vector Machine (SVM)

based iris quality enhancement algorithm [29]. The SVM

quality enhancement algorithm identifies good quality regions

from different globally enhanced iris images and combines

them to generate a single high quality feature-rich iris image.

Textural and topological features [17], [18] are then extracted

from the quality enhanced image for matching. Most of the iris

recognition algorithms extract features that provide only global

information or local information of iris patterns. In this paper,

the feature extraction algorithm extracts both global texture

features and local topological features. The texture features

are extracted using 1D log polar Gabor transform which

is invariant to rotation and translation, and the topological

features are extracted using Euler number which is invariant

under translation, rotation, scaling, and polar transformation.

The state-of-art iris recognition algorithms have a very

low false acceptance rate but reducing the number of false

rejection is still a major challenge. In multibiometric literature

[33], [34], [35], [36], it has been suggested that fusion of

information extracted from different classifiers provides better

performance compared to single classifiers. In this paper,

we propose using 2ν-SVM to develop a fusion algorithm

that combines the match scores obtained by matching texture

and topological features for improved performance. Further,

2

TABLE I

COMPARISON OF EXISTING IRIS RECOGNITION ALGORITHMS.

Research Quality Iris Image Feature extraction Additionalpaper assessment segmentation enhancement and matching comments

Daugman

[1]-[4]

Frequency Integro- Neural network + 2D Gabor First iris

approach differential operator - transform + Hamming distance recognition algorithm

Wildes [5]Using high Image intensity gradient - Laplacian of Gaussian filters + -contrast edges and Hough transform normalized correlation

Boles and - Edge - Wavelet transform zero crossing + Does not perform forBoashash [6] detection dissimilarity function non-ideal iris images

Ma et al.

[12]

Frequency based Gray-level information Background Multichannel spatial filter + fisher- Does not work

SVM classification and canny edge detection subtraction linear discriminant classification with occluded images

Ma et al.

[13]- Gray-level information Background 1D iris signal operated on Local features are

and canny edge detection subtraction Dyadic wavelet + similarity function used for recognition

Avila andReillo [16]

- Intensity based - Gabor filter and multiscale zero-crossing + Does not unwrapdetection Euclidean and Hamming distance the iris image

Vatsa et al.

[18]

- Intensity based - 1D log polar Gabor and Euler number + Rule based decisiondetection Hamming distance and L1 distance strategy is used

to improve accuracy

Monro et al.

[24]

- Heuristic gray-level Background 1D DCT + Hamming distance Fast feature extraction

edge feature subtraction and matching

Poursaberi and - Morphological operators Wiener Daubechies 2 wavelet + Hamming -Araabi [25] and thresholds 2D filter distance and harmonic mean

- Active contours and - Gaze deviation correction,Daugman [32] generalized coordinates - Iris Code second rank in ICE 2006

and low time complexity

recognition can be divided into verification and identification.

The performance of both verification and identification suffer

due to non-ideal acquisition issues. However, identification is

more difficult compared to verification because of the problem

of high penetration rate and false accept rate. To improve

the identification performance, we propose an iris indexing

algorithm. In the proposed indexing algorithm, the Euler code

is first used to filter possible matches. This subset is further

processed using the texture features and 2ν-SVM fusion for

accurate identification.

Section II presents the proposed non-ideal iris segmentation

algorithm and Section III describes the novel quality enhance-

ment algorithm. Section IV briefly explains the extraction of

global features using 1D log polar Gabor transform and the

extraction of local features using Euler number. Section V

describes the intelligent match score fusion algorithm and Sec-

tion VI presents the indexing algorithm to reduce the average

identification time. The details of iris databases and existing

algorithms used for validation of the proposed algorithm is

presented in Section VII. Section VIII and IX summarize the

verification and identification performance of the proposed

algorithms with existing recognition and fusion algorithms.

II. NON-IDEAL IRIS SEGMENTATION ALGORITHM

Processing non-ideal iris images is a challenging task be-

cause the iris and pupil are non-circular and the shape varies

depending on how the image is captured. The first step in

iris segmentation is the detection of pupil and iris boundaries

from the input eye image and unwrapping the extracted iris

into a rectangular form. Researchers have proposed different

algorithms for iris detection. Daugman [1] applied integro-

differential operator to detect the boundaries of iris and pupil.

The segmented iris is then converted into rectangular form by

applying polar transformation. Wildes [5] used the first deriva-

tive of image intensity to find the location of edges correspond-

ing to the iris boundaries. This system explicitly models the

upper and lower eyelids with parabolic arcs whereas Daugman

excludes the upper and the lower portions of the image. Boles

and Boashash [6] localized and normalized the iris by using

edge detection and other computer vision algorithms. Ma et

al. [12], [13] used Hough transform to detect the iris and pupil

boundaries. Normally, the pupil has a dark color and the iris

has a light color with varying pigmentation. In certain non-

ideal conditions, the iris can be dark and the pupil can appear

illuminated. For example, because of the specular reflections

from the cornea or co-axial illumination directly into the eye,

light is reflected into the retina and back through the pupil

which makes the pupil appear bright. Also, the boundary of

non-ideal iris image is irregular and cannot be considered

exactly circular or elliptical. For such non-ideal and irregular

iris images, researchers have recently proposed segmentation

algorithms which combine conventional intensity techniques

with active contours for pupil and iris boundary detection [32],

[37]-[39]. These algorithms use intensity based techniques for

center and pupil boundary detection. The pupil boundary is

used to initialize the active contour which evolves to find

the outer boundary of iris. This method of evolution from

pupil to the outer iris boundary is computationally expensive.

We therefore propose a 2-stage iris segmentation algorithm in

which we first estimate the inner and outer boundaries of iris

using an elliptical model. In the second stage, we apply the

modified Mumford Shah functional [40] in a narrow band over

the estimated boundaries to compute the exact inner and outer

boundaries of the iris.

To identify the approximate boundary of pupil in non-ideal

eye-images, an elliptical region with major axis a = 1, minor

axis b = 1, and center (x, y) is selected as the center of eye

and the intensity values are computed for a fixed number of

points on the circumference. The parameters of the ellipse

(a, b, x, y, θ) are iteratively varied with a step size of two

3

(a)

(b)

(c)

(d)

(e)

Fig. 1. Iris images representing the challenges of iris recognition (a) Iristexture occluded by eyelids and eyelashes, (b) Iris images of an individual

with different gaze direction, (c) Iris images of an individual showing theeffects of contraction and dilation, (d) Iris images of same individual at

different instances: First image is of good quality and second image hasmotion blurriness and limited information present, (e) Images of an individualshowing the effect of natural luminosity factor [26].

pixels to increase the size of ellipse and every time a fixed

number of points are randomly chosen on the circumference

(in the experiments, it is set to be 40 points) to calculate

the total intensity value. This process is repeated to find

the boundary with maximum variation in intensity and the

center of the pupil. The approximate outer boundary of the

iris is also detected in a similar manner. The parameters for

outer boundary a1, b1, x1, y1, and θ1 are varied by setting

the initial parameters to the pupil boundary parameters. A

fixed number of points (in the experiments, it is set to be

120 points) are chosen on the circumference and the sum

of the intensity values is computed. Values corresponding

to the maximum intensity change give the outer boundary

of the iris and the center of this ellipse gives the center

of the iris. This method thus provides approximate iris and

pupil boundaries, corresponding centers, and major and minor

axis. Some researchers assume the center of pupil to be the

center of iris and compute the outer boundary. While this

helps to simplify the modeling, in reality, this assumption

is not valid for non-ideal iris. Computing outer boundary

using the proposed algorithm provides accurate segmentation

even when the pupil and iris are not concentric. Using these

approximate inner and outer boundaries, we now perform the

curve evolution with modified Mumford-Shah functional [40],

[41] for iris segmentation.

In the proposed curve evolution method for iris segmenta-

tion, the model begins with the following energy functional:

Energy(c) = α

∫

Ω

||∂C

∂c|| φ dc+

β

∫∫

in(C)

|I(x, y) − c1|2dxdy

+λ

∫∫

out(C)

|I(x, y) − c2|2dxdy (1)

where C is the evolution curve such that C = (x, y) :ψ(x, y) = 0, c is the curve parameter, φ is the weighting

function or stopping term, Ω represents the image domain,

I(x, y) is the original iris image, c1 and c2 are the average

value of pixels inside and outside C respectively, and α,

β, and λ are positive constants such that α < β ≤ λ.

Parameterizing Equation 1 and deducing the associated Euler-

Lagrange equation leads to the following active contour model,

ψt = αφ(ν+εk)|5 ψ|+5φ5 ψ+βδ(I−c1)2 +λδψ(I−c2)

2

(2)

where ν is the advection term, εk is the curvature based

smoothing term, 5 is the gradient operator and δ =0.5/(π(x2 + 0.25)). The stopping term φ is defined as,

φ =1

1 + (| 5 I|)2(3)

The active contour ψ is initialized to the approximate

pupil boundary and the exact pupil boundary is computed by

evolving the contour in a narrow band [42] of ±5 pixels.

Similarly, for computing the exact outer iris boundary, the

approximate iris boundary is used as the initial contour ψ and

the curve is evolved in a narrow band [42] of ±10 pixels.

Using the stopping term, φ, the curve evolution stops at the

exact outer iris boundary. Since we are using the approximate

iris boundaries as the initial ψ, the complexity of curve

evolution is reduced and is suitable for real-time applications.

Fig. 2 shows the pupil and iris boundaries extracted using the

proposed non-ideal iris segmentation algorithm.

In non-ideal cases, eyelids and eyelashes may be present

as noise and decrease the recognition performance. Using the

technique described in [1], eyelids are isolated by fitting lines

to the upper and lower eyelids. A mask based on the detected

4

Fig. 2. Iris detection using the proposed non-ideal iris segmentation algorithm.

eyelids and eyelashes is then used to extract the iris without

noise. Image processing of iris is computationally intensive

as the area of interest is of donut shape and grabbing the

pixels in this region requires repeated rectangular to polar

conversion. To simplify this, the detected iris is unwrapped

into a rectangular region by converting into polar coordinates.

Let I(x, y) be the segmented iris image and I(r, θ) be the

polar representation obtained using Equations 4 and 5.

r =√

(x− xc)2 + (y − yc)2 0 ≤ r ≤ rmax (4)

θ = tan−1

(

y − yc

x− xc

)

(5)

r and θ are defined with respect to the center coordinates,

(xc, yc). The center coordinates obtained during approximate

elliptical iris boundary fitting are used as the center point for

cartesian to polar transformation. The transformed polar iris

image is further used for enhancement, feature extraction, and

matching.

III. GENERATION OF SINGLE HIGH QUALITY IRIS IMAGE

USING ν -SUPPORT VECTOR MACHINE

For iris image enhancement, researchers consecutively apply

selected enhancement algorithms such as deblurring, denois-

ing, entropy correction, and background subtraction, and use

the final enhanced image for further processing. Huang et

al. [43] used super-resolution and Markov network for iris

image quality enhancement but their method does not perform

well with unregistered iris images. Ma et al. [12] proposed

background subtraction based iris enhancement that filters the

high frequency noise. Poursaberi and Araabi [25] proposed the

use of low pass Wiener 2D filter for iris image enhancement.

However, these filtering techniques are not effective in mit-

igating the effects of blur, out of focus, and entropy based

irregularities. Another challenge with existing enhancement

techniques is that they enhance the low quality regions present

in the image but are likely to deteriorate the good quality

regions and alter the features of the iris image. A non-ideal

iris image containing multiple irregularities may require the

application of specific algorithms to local regions that need

enhancement. However, identifying and isolating these local

regions in an iris image can be tedious, time consuming, and

not pragmatic. In this paper, we address the problem by con-

currently applying a set of selected enhancement algorithms

globally to the original iris image [29]. Thus each resulting

image contains enhanced local regions. These enhanced local

regions are identified from each of the transformed images

using support vector machine [44] based learning algorithm

and then synergistically combined to generate a single high

quality iris image.

Let I be the original iris image. For every iris image in the

training database, a set of transformed images is generated by

applying standard enhancement algorithms for noise removal,

defocus, motion blur removal, histogram equalization, entropy

equalization, homomorphic filtering, and background subtrac-

tion. The set of enhancement functions is expressed as,

I1 = fnoise(I)

I2 = fblur(I)

I3 = ffocus(I)

I4 = fhistogram(I) (6)

I5 = fentropy(I)

I6 = ffilter(I)

I7 = fbackground(I)

where fnoise is the algorithm for noise removal, fblur is

the algorithm for blur removal, ffocus is the algorithm for

adjusting the focus of the image, fhistogram is the histogram

equalization function, fentropy is the entropy filter, ffilter

is the homomorphic filter for contrast enhancement, and

fbackground is the background subtraction process. I1, I2,

I3, I4, I5, I6, and I7 are the resulting globally enhanced

images obtained when the above enhancement operations are

applied to the original iris image I. Applying several global

enhancement algorithms does not uniformly enhance all the

regions of the iris image. A learning algorithm is proposed

to train and classify the pixel quality from corresponding

locations of the globally enhanced iris images. This knowledge

is used by the algorithm to identify the good quality regions

from each of the transformed and original iris images, and

combined to form a single high quality iris image. The learning

algorithm uses ν-SVM [45] which is expressed as,

5

f(x) = sgn

(

m∑

i=1

αiyik(x, xi) + b

)

m∑

i=1

αiyi = 0 (7)

m∑

i=1

αi ≥ ν

where ν ε [0, 1], xi is the input to ν-SVM, yi is the

corresponding label, m is the number of tuples, αi is the dual

variable, and k is the RBF kernel. Further, a fast implementa-

tion of ν-SVM [46] is used to decrease the time complexity.

Training involves classifying the local regions of the input

and global enhanced iris image as good or bad. Any quality

assessment algorithm can be used for this task. However, in

this research we have used the redundant discrete wavelet

transformation based quality assessment algorithm described

in [47]. To minimize the possibility of errors due to the quality

assessment algorithm, we also verify the labels manually and

correct them in case of errors. The labeled training data is then

used to train the ν-SVM. The training algorithm is described

as follows:

• The training iris images are decomposed to l levels using

Discrete Wavelet Transform. The 3l detail subbands of

each image contain the edge features and thus these bands

are used for training.

• The subbands are divided into windows of size 3×3 and

the activity level of each window is computed.

• The ν-SVM is trained using labeled iris images to deter-

mine the quality of every wavelet coefficient. The activity

levels computed in the previous step are used as input to

the ν-SVM.

• The output of training algorithm is ν-SVM with a separat-

ing hyperplane. The trained ν-SVM labels the coefficient

G or 1 if it is good and B or 0 if the coefficient is bad.

Next the trained ν-SVM is used to classify the pixels from

input image and to generate a new feature-rich high quality iris

image. The classification algorithm is described as follows:

• The original iris image and the corresponding globally

enhanced iris images generated using Equation 6 are

decomposed to l DWT levels.

• The ν-SVM classifier is then used to classify the co-

efficients of the input bands as good or bad. A decision

matrix, Decision, is generated to store the quality of each

coefficient in terms of G and B. At any position (i, j), if

the SVM output O(i, j) is positive then that coefficient

is labeled as G, otherwise it is labeled as B.

Decision(i, j) =

G if O(i, j) ≥ 0B if O(i, j) < 0

(8)

• The above operation is performed on all eight images

including the original iris image and a decision matrix

corresponding to every image is generated.

• For each of the eight decision matrices, the average

of all coefficients with label G is computed and the

coefficients having label B are discarded. In this man-

ner, one fused approximation band and 3l fused detail

subbands are generated. Individual processing of every

coefficient ensures that the irregularities present locally

in the image are removed. Further, the selection of good

quality coefficients and removal of all bad coefficients

addresses multiple irregularities present in one region.

• Inverse DWT is applied on the fused approximation and

detail subbands to generate a single feature-rich high

quality iris image.

In this manner, the quality enhancement algorithm enhances

the quality of the input iris image and a feature-rich image is

obtained for feature extraction and matching. Fig. 3 shows an

example of the original iris image, different globally enhanced

images, and the combined image generated using the proposed

iris image quality enhancement algorithm.

IV. IRIS TEXTURE AND TOPOLOGICAL FEATURE

EXTRACTION AND MATCHING ALGORITHMS

Researchers have proposed several feature extraction al-

gorithms to extract unique and invariant features from iris

image. These algorithms use either texture or appearance

based features. The first algorithm was proposed by Daugman

[1] which used 2D Gabor for feature extraction. Wildes [5]

applied isotropic band-pass decomposition derived from the

application of Laplacian of Gaussian filters to the iris image.

It was followed by several different research papers such as,

Ma et al. [12], [13] in which multichannel even-symmetric

Gabor wavelet and the multichannel spatial filters were used to

extract textural information from iris patterns. The usefulness

of the iris features depends on the properties of basis function

and the feature encoding process.

In this paper, the iris recognition algorithm uses both global

and local properties of an iris image. 1D log polar Gabor

transform [48] based texture feature [17], [18] provides the

global properties which are invariant to scaling, shift, rotation,

illumination, and contrast. Topological features [17], [18]

extracted using Euler number [49] provide local information

of iris patterns and are invariant to rotation, translation, and

scaling of the image. The following two subsections briefly

describe the textural and topological feature extraction algo-

rithm.

1) Texture Feature Extraction using 1D Log Polar Ga-

bor Wavelet: The texture feature extraction algorithm [17],

[18] uses 1D log polar Gabor transform [48]. Like Gabor

transform [50], log polar Gabor transform is also based on

polar coordinates but unlike the frequency dependence on a

linear graduation, the dependency is realized by a logarithmic

frequency scale [50], [51]. Therefore, the functional form of

1D log polar Gabor transform is given by:

Gr0θ0(θ) = exp

[

−2π2σ2

[

ln( r−r0

f )2

τ2 +2ln(f0sin(θ−θ0))2

]]

(9)

where (r, θ) are the polar coordinates, r0 and θ0 are the initial

values, f is the center frequency of the filter and f0 is the

parameter that controls the bandwidth of the filter. σ and τare defined as follows:

6

Fig. 3. Original iris image, seven globally enhanced images and the SVM enhanced iris image.

σ =1

πln(r0)sin(π/θ0)

√

ln2

2(10)

τ =2ln(r0)sin(π/θ0)

ln2

√

ln2

2(11)

Gabor transform is symmetric with respect to the principal

axis. During encoding, Gabor function over-represents the

low frequency components and under-represents the high

frequency components [48], [50], [51]. In contrast, log polar

Gabor transform shows maximum translation from center of

gravity in the direction of lower frequency and flattening of the

high frequency part. The most important feature of this filter

is invariance to rotation and scaling. Also, log polar Gabor

functions have extended tails and encode natural images more

efficiently than Gabor functions.

To generate an iris template from 1D log polar Gabor

transform, the 2D unwrapped iris pattern is decomposed into a

number of 1D signals where each row corresponds to a circular

ring on the iris region. For encoding, angular direction is used

rather than radial direction because maximum independence

occurs along this direction. 1D signals are convolved with 1D

log polar Gabor transform in frequency domain. The values of

the convolved iris image are complex in nature. Using these

real and imaginary values, the phase information is extracted

and encoded in a binary pattern. If the convolved iris image

is Ig(r, θ), then the phase feature P (r, θ) is computed using

Equation 12.

P (r, θ) = tan−1

(

Im Ig(r, θ)

Re Ig(r, θ)

)

(12)

Ip(r, θ) =

[1, 1] if 00 < P (r, θ) ≤ 900

[0, 1] if 900 < P (r, θ) ≤ 1800

[0, 0] if 1800 < P (r, θ) ≤ 2700

[1, 0] if 2700 < P (r, θ) ≤ 3600

(13)

Phase features are quantized using the phase quantization

process represented in Equation 13 where Ip(r, θ) is the

resulting binary iris template of 4096 bits. Fig. 4 shows the

iris template generated using this algorithm.

Fig. 4. Binary iris templates generated using 1D log polar Gabor transform.

(a) and (b) are iris templates of the same individual at two different instances.

To verify a person’s identity, the query iris template is

matched with the stored templates. For matching the textural

iris templates, we use Hamming distance [1]. The match score,

MStexture, for any two texture-based masked iris templates,

Ai and Bi, is computed using Hamming distance measure

given by Equation 14.

MStexture =1

N

N∑

i=1

Ai ⊕Bi (14)

where N is the number of bits represented by each template

and ⊕ is the XOR operator. For handling rotation, the tem-

plates are shifted left and right bitwise and the match scores

are calculated for every successive shift [1]. The smallest

value is used as the final match score, MStexture. The bitwise

7

shifting in the horizontal direction corresponds to rotation of

the original iris region at an angle defined by the angular

resolution. This also takes into account the misalignments

in the normalized iris pattern which are caused due to the

rotational differences during imaging.

2) Topological Feature Extraction using Euler Number:

Convolution with 1D log polar Gabor transform extracts the

global textural characteristics of the iris image. To further

improve the performance, local features represented by the

topology of iris image are extracted using Euler numbers [18],

[49]. For a binary image, Euler number is defined as the

difference between the number of connected components and

the number of holes. Euler numbers are invariant to rotation,

translation, scaling, and polar transformation of the image

[18]. Each pixel of the unwrapped iris can be represented

as an 8-bit binary vector b7, b6, b5, b4, b3, b2, b1, b0. These

bits form eight planes with binary values. As shown in Fig. 5,

four planes formed from the four most significant bits (MSB)

represent the structural information of iris, and the remaining

four planes represent the brightness information [49]. The

brightness information is random in nature and is not useful

for comparing the structural topology of two iris images.

Fig. 5. Binary images corresponding to eight bit planes of the masked polar

image.

For comparing two iris images using Euler code, a common

mask is generated for both the iris images to be matched.

The common mask is generated by performing a bitwise-OR

operation of the individual masks of the two iris images and

is then applied to both the polar iris images. For each of the

two iris images with common mask, a 4-tuple Euler code is

generated which represents the Euler number of the image

corresponding to the four MSB planes. Table II shows the

Euler Codes of a person at three different instances.

We use Mahalanobis distance to match the two Euler codes.

Mahalanobis distance between two vectors is defined as,

D(x, y) =

√

(x− y)t S−1 (x− y) (15)

where, x and y are the two Euler codes to be matched and

S is the positive definite covariance matrix of x and y. If

TABLE II

EULER CODE OF AN INDIVIDUAL AT THREE DIFFERENT INSTANCES.

Euler codeMSB1 MSB2 MSB3 MSB4

Image 1 90 -73 127 -347

Image 2 89 -67 118 -355

Image 3 91 -82 135 -343

the Euler code has large variance, it increases the false reject

rate. Mahalanobis distance ensures that the features having

high variance do not contribute to the distance. Applying

Mahalanobis distance measure for comparison thus avoids the

increase in false reject rate. The topology based match score

is computed as,

MStopology =D(x, y)

log10max(D)(16)

where, max(D) is the maximum possible value of Maha-

lanobis distance between two Euler codes. The match score of

Euler codes is the normalized Mahalanobis distance between

two Euler codes.

V. FUSION OF TEXTURE AND TOPOLOGICAL MATCHING

SCORES

Iris recognition algorithms have succeeded in achieving

a low false acceptance rate but reducing the rejection rate

remains a major challenge. To make iris recognition algorithms

more practical and adaptable to diverse applications, the false

rejection rate needs to be reduced significantly. In [33], [35],

[36], [52], it has been suggested that the fusion of match scores

from two or more classifiers provides better performance

compared to a single classifier. In general, match score fusion

is performed using sum rule, product rule, or other statistical

rules. Recently in [35], a kernel based match score fusion

algorithm has been proposed to fuse the match scores of

fingerprint and signature. In this section, we propose using

2ν-SVM [53] to fuse the information obtained by matching

the textural and the topological features of iris image that

are described in Section IV. The proposed fusion algorithm

reduces the false rejection rate while maintaining a low false

acceptance rate.

Let the training set be Z = (xi, yi) where i = 1, ..., N .

N is the number of multimodal scores used for training and

yi ∈ (1,−1), where 1 represents the genuine class and -

1 represents the impostor class. SVM is trained using these

labeled training data. The mapping function ϕ(·) is used to

map the training data into a higher dimensional feature space

such that Z → ϕ(Z). The optimal hyperplane which separates

the higher dimensional feature space into two different classes

in the higher dimensional feature space can be obtained using

2ν-SVM [53].

We have xi, yi as the set of N multimodal scores with

xi ε <d. Here, xi is the ith score that belongs to the binary

class yi . The objective of training 2ν-SVM is to find the

hyperplane that separates two classes with the widest margins,

i.e.,

8

wϕ(x) + b = 0 (17)

subject to,

yi (wϕ(x) + b) ≥ (ρ− ψi), ξi ≥ 0 (18)

to minimize,

1

2‖w‖2 −

∑

i

Ci(νρ− ξi) (19)

where ρ is the position of margin and ν is the error parameter.

ϕ(x) is the mapping function used to map the data space to

the feature space and provide generalization for the decision

function that may not be a linear function of the training data.

Ci(νρ−ξi) is the cost of errors, w is the normal vector, b is the

bias, and ξi is the slack variable for classification errors. ν can

be calculated using ν+ and ν−, which are the error parameters

for training the positive and negative classes respectively.

ν =2ν+ ν−ν+ + ν−

, 0 < ν+ < 1 and 0 < ν− < 1 (20)

Error penalty Ci is calculated as,

C =

C+, if yi = +1C−, if yi = −1

(21)

where,

C+ =

[

n+

(

1 +ν+

ν−

)]−1

(22)

C− =

[

n−

(

1 +ν−ν+

)]−1

(23)

and n+ and n− are the number of training points for the

positive and negative classes respectively. 2ν-SVM training

can be formulated as,

max(αi)

−1

2

∑

i,j

αi αj yi yj K(xi, xj)

(24)

where,0 ≤ αi ≤ Ci

∑

i αiyi = 0

∑

i αi ≥ ν

(25)

i, j ε 1, ..., N and kernel function is

K (xi, xj) = ϕ(xi)ϕ(xj) (26)

Kernel function K(xi, xj) is chosen as the radial basis func-

tion. The 2ν-SVM is initialized and optimized using iterative

decomposition training [53], which leads to reduced complex-

ity.

In the testing phase, fused score ft of a multimodal test

pattern xt is defined as,

ft = f (xt) = wϕ (xt) + b (27)

The solution of this equation is the signed distance of xt

from the separating hyperplane given by 2ν-SVM. Finally, an

accept or reject decision is made on the test pattern xt using

a threshold X:

Result(xt) =

accept, if output of SV M ≥ Xreject, if otherwise

(28)

Fig. 6 presents the steps involved in the proposed 2ν-SVM

learning algorithm which fuses the texture and topological

match scores for improved classification.

VI. IRIS IDENTIFICATION USING EULER CODE INDEXING

Iris recognition can be used for verification (1:1 matching)

as well as identification (1:N matching). Apart from the irreg-

ularities due to non-ideal acquisition, iris identification suffers

from high system penetration and false accept cases. For

identification, a probe iris image is matched with all the gallery

images and the best match is rank #1 match. Due to the poor

quality and non-ideal acquisition, rank 1 match may not be the

correct match and lead to false acceptance. The computational

time for performing iris identification on large databases is

another challenge [31]. For example, identifying an individual

from a database of 50 million users requires an average of

25 million comparisons. On such databases, applying distance

based iris code matching or the proposed SVM fusion will take

significant amount of time. Parallel processing and improved

hardware can reduce the computational time at the expense of

operational cost.

Other techniques which can be used to speedup the iden-

tification process are classification and indexing. Yu et al.

[19] proposed a coarse iris classification technique using

fractals which classifies iris images into four categories. The

classification technique improves the performance in terms

of computational time but compromises the identification

accuracy. Mukherjee [54] proposed an iris indexing algorithm

in which block based statistics is used for iris indexing. Single

pixel difference histogram used in the indexing algorithm

yields good performance on a subset of CASIA version 3.0

database. However, the indexing algorithm is not evaluated for

non-ideal poor quality iris images.

In this paper, we propose feature based iris indexing al-

gorithm for reducing the computational time required for iris

identification without compromising the identification accu-

racy. The proposed indexing algorithm is a two step process

where the Euler code is first used to generate a small subset of

possible matches. The 2ν-SVM match score fusion algorithm

is then used to find the best matches from the list of possible

matches. The proposed indexing algorithm is divided into two

parts: (1) feature extraction and database enrollment in which

features are extracted from the gallery images and indexed

using Euler code and (2) probe image identification in which

features from the probe image are extracted and matched.

A. Feature Extraction and Database Enrollment

Compared to the feature extraction and matching algorithm

described in Section IV, we use a slightly different strat-

egy for feature extraction. For verification, we use common

9

Fig. 6. Steps involved in the proposed 2ν-SVM match score fusion algorithm.

masks from gallery and probe images to hide the eyelids

and eyelashes. However, in indexing, we do not follow the

same method because generating common mask for every set

of probe and gallery images will increase the computational

cost. Using the iris center coordinates, the X and Y axis are

drawn and the iris is divided into four regions. Researchers

have shown that regions A and B in Fig. 7 contain minimum

occlusion due to eyelids and eyelashes, and hence are the most

useful for iris recognition [4], [9], [22]. Therefore for indexing

we use regions A and B to extract features. The extracted

features are stored in the database and Euler code is used as

the indexing parameter.

A B

A B

A B

A B

Fig. 7. Iris image divided into four parts. Regions A and B are used in theproposed iris indexing algorithm.

B. Probe Image Identification

Similar to the database enrollment process, features are

extracted from the probe iris image and Euler code is used

to find the possible matches. For matching two iris indexing

parameters (Euler codes), E1(i) and E2(i) (i = 1, 2, 3, 4),

we apply a thresholding scheme. Indexing parameters are

said to be matched if |E1(i) − E2(i)| ≤ T where T is the

geometric tolerance constant. Indexing score S is computed

using Equations 29 and 30.

s(i) =

1 if |E1(i) − E2(i)| ≤ T0 otherwise

(29)

S =1

4

4∑

i=1

s(i) (30)

where, |s| = 4 is the intermediate score vector that provides

the number of matched Euler values. We extend this scheme

for iris identification by matching the indexing parameter of

the probe image with the gallery images. Let n be the total

number of gallery images and Sn represent the indexing scores

corresponding to the n comparisons. The indexing scores, Sn,

are sorted in descending order and the top M match scores

are selected as possible matches.

For every probe image, the Euler code based indexing

scheme yields a small subset of top M matches from the

gallery where M << n (for instance, M = 20 and n =2000). To further improve the identification accuracy, we apply

the proposed 2ν-SVM match score fusion. We then use the

algorithms described in Sections IV and V to match the

textural and topological features of the probe image with top

M matched images from the gallery and compute the fused

match score for each of the M gallery images. Finally, these

M fused match scores are again sorted and a new ranking is

obtained to determine the identity.

VII. DATABASES AND ALGORITHMS USED FOR

PERFORMANCE EVALUATION AND COMPARISON

In this section, we describe the iris databases and algo-

rithms used for evaluating the performance of the proposed

algorithms.

A. Databases used for Validation

To evaluate the performance of the proposed algorithms, we

selected three iris databases namely ICE 2005 [30], [31], CA-

SIA Version 3 [55], and UBIRIS [26], [27]. These databases

are chosen for validation because the iris images embody

irregularities captured with different instruments and device

characteristics under varying conditions. The databases also

contain iris images from different ethnicity and facilitates a

comprehensive performance evaluation of the proposed algo-

rithms.

• ICE 2005 database [30], [31] used in recent Iris Challenge

Evaluation contains iris images from 244 iris classes. The

total number of images present in the database is 2,953.

• CASIA Version 3 database [55] contains 22,051 iris

images pertaining to more than 1,600 classes. The images

10

have been captured using different imaging setup. The

quality of images present in the database also varies

from high quality images with extremely clear iris texture

details to images with nonlinear deformation due to vari-

ations in visible illumination. Unlike the CASIA Version

1 where artificially manipulated images were present, the

CASIA Version 3 contains original unmasked images.

• UBIRIS database [26], [27] is composed of 1,877 images

from 241 classes captured in two different sessions. The

images in the first session are of good quality whereas the

images captured in the second session have irregularities

in reflection, contrast, natural luminosity and focus.

B. Existing Algorithms used for Validation

To evaluate the effect of the proposed quality enhance-

ment algorithm on different feature extraction and matching

techniques, we implemented Daugman’s integro-differential

operator and neural network architecture based 2D Gabor

transform described in [1]-[4]. We also used the Masek’s

iris recognition algorithm obtained from [11]. Further, the

performance of the proposed 2ν-SVM fusion algorithm is

compared with Sum rule [33], [34], Min/Max rule [33], [34],

and kernel based fusion rule [35].

VIII. PERFORMANCE EVALUATION AND VALIDATION FOR

IRIS VERIFICATION

In this section, we evaluate the performance of the proposed

segmentation, enhancement, feature extraction, and fusion al-

gorithms for iris verification. The performance of the proposed

algorithms is validated using the databases and algorithms

described in Section VII. For validation, we divided the

databases into three parts: training dataset, gallery dataset, and

probe dataset. Training dataset consist of manually labeled one

good quality and one bad quality image per class. This dataset

is used to train the ν-SVM for quality enhancement and 2ν-

SVM for fusion. After training, the good quality image in the

training dataset is used as the gallery dataset and the remaining

images are used as the probe dataset. The bad quality image

of the training dataset is not used for either gallery or probe

dataset.

For iris segmentation, we performed extensive experiments

to compute a common set of curve evolution parameters that

can be applied to detect exact boundaries of iris and pupil

from all the databases. The values of different parameters

for segmentation with narrow band curve evolution are α =0.2, β = 0.4, λ = 0.4, advection term ν = 0.72, and

curvature term εk = 0.001. These values provide accurate

segmentation results for all three databases. Fig. 8 shows

sample results demonstrating the effectiveness of the proposed

iris segmentation algorithm on all the databases with different

characteristics. The inner yellow curve represents the pupil

boundary and the outer red curve represents the iris boundary.

Fig. 8 also shows that the proposed segmentation algorithm is

not affected by different types of specular reflections present

in the pupil region.

Using the proposed iris segmentation and quality enhance-

ment algorithms, we then evaluated the verification perfor-

mance with the textural and topological features. The match

CASIA Version 3 Database

UBIRIS Database

ICE 2005 Database

Fig. 8. Results of the proposed iris segmentation algorithm.

scores obtained from texture and topological features were

fused using 2ν-SVM to further evaluate the proposed fusion

algorithm. Figs. 9-11 show the ROC plots for iris recogni-

tion using the textural feature extraction, topological feature

extraction, and 2ν-SVM match score fusion algorithms.

Fig. 9 shows the ROC plot for the ICE 2005 database

[30] and Fig. 10 shows the results for the CASIA Version 3

database [55]. The ROC plots show that the proposed 2ν-SVM

match score fusion performs the best followed by the textural

and topological features based verification. The false rejection

rate of individual features is high but the fusion algorithm

reduces it significantly and provides the FRR of 0.74% at

0.0001% false accept rate (FAR) on the ICE 2005 database and

0.38% on the CASIA Version 3 database. The results on the

ICE 2005 database also show that the verification performance

of the proposed fusion algorithm is comparable to the three

best algorithms in the Iris Challenge Evaluation 2005 [31].

The same set of experiments is performed using the UBIRIS

database [26]. The images in this database contain irregular-

ities due to motion blur, off angle, gaze direction, diffusion,

and other real world problems that enable us to evaluate the

robustness of the proposed algorithms on non-ideal iris images.

Fig. 11 shows the ROC plot obtained using the UBIRIS

database. In this experiment, the best performance of 7.35%

FRR at 0.0001% FAR is achieved using the 2ν-SVM match

score fusion algorithm. The high rate of false rejection is due

to cases where the iris is partially visible. Examples of such

cases are shown in Fig. 12.

The experimental results on all three databases are summa-

rized in Table III. In this table, it can be seen that the proposed

fusion algorithm significantly reduces the false rejection rate.

11

Fig. 12. Sample iris images from the UBIRIS database [26] on which the proposed algorithms fail to perform.

TABLE IV

EFFECT OF THE PROPOSED IRIS IMAGE QUALITY ENHANCEMENT ALGORITHM AND PERFORMANCE COMPARISON OF IRIS RECOGNITION ALGORITHMS.

False rejection rate (%) at 0.0001% false accept rate with different enhancement algorithms

Daugman’s implementation [1], [4] Masek’s algorithm [11] Proposed 2ν-SVM fusion algorithmDatabase None Wiener Background Proposed None Wiener Background Proposed None Wiener Background Proposed

filter subtraction SVM filter subtraction SVM filter subtraction SVM

CASIA 3 4.87 3.19 3.14 2.37 16.64 15.91 15.18 11.80 1.61 1.29 1.24 0.38

ICE 2005 2.11 1.81 1.73 0.98 12.34 11.88 11.73 9.62 1.99 1.64 1.63 0.74

UBIRIS 12.96 12.10 12.09 8.71 18.85 17.09 16.99 14.11 9.13 8.82 8.79 7.35

0 0.001 0.002 0.003 0.0040

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

False Accept Rate(%)

Fa

lse

Re

jectio

n R

ate

(%)

Topological

Texture

SVM − Match Score

Fig. 9. ROC plot showing the performance of the proposed algorithms onthe ICE 2005 database [30].

However, the rejection rate cannot be reduced if a closed eye

image or an eye image with limited information is present for

matching.

We next evaluated the effectiveness of the proposed iris

image quality enhancement algorithm and compared with

existing enhancement algorithms namely Wiener filtering [25]

and background subtraction [12]. Table IV shows the results

for the proposed and existing verification algorithms when the

original iris image is used and when the quality enhanced im-

ages are used. For the ICE 2005 database, this table shows that

without enhancement, the proposed 2ν-SVM fusion algorithm

gives 1.99% FRR at 0.0001% FAR. The performance improves

by 1.25% when the proposed iris image quality enhancement

algorithm is used. We also found that the proposed SVM

image quality enhancement algorithm outperforms existing

0 0.001 0.002 0.004 0.006 0.008 0.010 0.012 0.014 0.016 0.0180

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

False Accept Rate(%)

Fa

lse

Re

jectio

n R

ate

(%)

Topological

Texture

SVM − Match Score

Fig. 10. ROC plot showing the performance of the proposed algorithms onthe CASIA Version 3 database [55].

enhancement algorithms by at least 0.89%. Similar results are

obtained for other two iris image databases. SVM iris image

quality enhancement algorithm also improves the performance

of existing iris recognition algorithms. SVM enhancement

algorithm performs better because SVM locally removes the

irregularities such as blur and noise, and enhances the intensity

of the iris image whereas Wiener filter only removes the

noise and background subtraction algorithm only highlights

the features by improving the image intensity.

We further compared the performance of the proposed 2ν-

SVM fusion algorithm with Daugman’s iris detection and

recognition algorithms [1]-[4] and Masek’s implementation of

iris recognition [11]. The results in Table IV show that the

proposed 2ν-SVM fusion yields better performance compared

to the Daugman’s and Masek’s implementation because the

12

0 2 4 6 8 10 12 14 16 18 200

2

4

6

8

10

12

14

16

18

20

False Accept Rate(%)

Fa

lse

Re

jectio

n R

ate

(%)

Topological

Texture

SVM − Match Score

Fig. 11. ROC plot showing the performance of the proposed algorithms onthe UBIRIS iris database [26].

TABLE III

PERFORMANCE COMPARISON OF THE PROPOSED ALGORITHMS ON THREE

IRIS DATABASES.

FRR (%) at 0.0001% FAR

Algorithm CASIA 3 ICE 2005 UBIRISdatabase database database

Topological features 1.81 4.41 17.83

Texture features 0.77 2.37 11.72

2ν-SVM match score fusion 0.38 0.74 7.35

2ν-SVM fusion algorithm uses multiple cues extracted from

the iris image and intelligently fuses the matching scores such

that the false rejection is reduced without increasing the false

acceptance rate. Higher performance of the proposed algorithm

is also due to the accurate iris segmentation obtained using the

modified Mumford Shah functional.

Further, the performance of the proposed 2ν-SVM fusion

algorithm is compared with Sum rule [33], [34], Min/Max

rule [33], [34], and kernel based fusion algorithms [35]. The

performance of the proposed and existing fusion algorithms is

evaluated on the ICE 2005 database by fusing the match scores

obtained from the texture and topological features. Table V

shows that the proposed 2ν-SVM fusion algorithm performs

the best with 0.74% FRR at 0.0001% FAR which is 0.74%

better than kernel based fusion algorithm [35] and 0.83% better

than Sum rule [33]. These results thus show that the proposed

fusion algorithm effectively fuses the textural and topological

features of iris image, enhances the recognition performance,

and reduces the false rejection rate considerably. The average

time for matching two iris images is 1.56 seconds on Pentium-

IV 3.2 GHz processor with 1 GB RAM under C programming

environment. Table VI shows the breakdown of computational

complexity in terms of the average execution time for iris

segmentation, enhancement, feature extraction and matching,

and 2ν-SVM fusion.

TABLE V

COMPARISON OF EXISTING FUSION ALGORITHMS WITH THE PROPOSED

2ν-SVM FUSION ALGORITHM ON THE ICE 2005 DATABASE.

Fusion Algorithm FRR at 0.0001% FAR (%)

Min/Max rule [33], [34] 1.91

Sum rule [33], [34] 1.57

Kernel based fusion [35] 1.48

Proposed 2ν-SVM fusion 0.74

TABLE VI

AVERAGE TIME TAKEN FOR THE STEPS INVOLVED IN THE PROPOSED IRIS

RECOGNITION ALGORITHM.

Algorithm Time (ms)

Iris segmentation 908

ν-SVM quality enhancement 347

Feature extraction and matching 211

2ν-SVM fusion 94

Average execution time 1560

IX. PERFORMANCE EVALUATION AND VALIDATION FOR

IRIS IDENTIFICATION

In this section, we present the performance of the proposed

indexing algorithm for iris identification. Similar to verifica-

tion, we use segmentation, enhancement, feature extraction,

and fusion algorithms described in Sections II - V. To validate

the performance of the proposed iris indexing algorithm,

we combine the three iris databases and generate a non-

homogeneous database with 2085 classes and 26,881 images.

The experimental setup (training dataset, gallery dataset, probe

dataset, and segmentation parameters) is similar to the setup

used for iris verification.

Using the training dataset, we found the value of geometric

tolerance constant T = 20. Fig. 13 shows the Cumulative

Matching Characteristics (CMC) plots for the proposed in-

dexing algorithm with and without the 2ν-SVM match score

fusion. The plots show that rank #1 identification accuracy of

92.39% is achieved when indexing algorithm is used without

match score fusion. The accuracy improves to 97.21% with

the use of 2ν-SVM match score fusion. Incorporating textural

features and match score fusion thus reduces the false accept

rate and provides an improvement of around 5% in rank #1identification accuracy. We also observed that on the non-

homogeneous database, 100% accuracy could not be achieved

because the database contains occluded images with very

limited information similar to those shown in Fig. 12.

We next compared the identification performance of Daug-

man’s iriscode algorithm and the proposed 2ν-SVM match

score fusion without indexing. Daugman’s algorithm is used

as a baseline for comparison. Daugman’s algorithm yields

the identification accuracy of 95.89% and the average time

required for identifying an individual is 5.58 seconds. On

the other hand, the identification accuracy of the proposed

2ν-SVM match score fusion algorithm without indexing is

97.21%. However, the average time for identifying an indi-

vidual is 221.14 seconds which is considerably higher than

Daugman’s algorithm. To reduce the significant time taken for

identification, the proposed indexing algorithm described in

Section VI is used. Indexing is achieved by using the Euler

13

2 4 6 8 10 12 14 16 18 2090

91

92

93

94

95

96

97

98

99

100

Top M Matches (Rank)

Ide

ntifica

tio

n A

ccu

racy (

%)

Indexing without Match Score Fusion (Case 1)

Indexing with Match Score Fusion (Case 3)

Fig. 13. CMC plot showing the identification accuracies obtained by theproposed indexing algorithm.

code which is computed from the local topological features

of the iris image. The indexing algorithm identifies a small

subset of the most likely candidates that will yield a match.

Specifically, we analyze three scenarios. Case 1 determines

a match based on the local topological feature match score.

Case 2 is an extension that uses the subset of images identified

with the local features. However, the matching is based on

the global texture feature match score. Case 3 is a further

extension that fuses the match scores obtained from the local

and global features to perform identification. The identification

performance is determined by experimentally computing the

accuracy and the time taken for identification. The results are

summarized in Table VII for all three cases when indexing

is used with the proposed recognition algorithm. In all three

scenarios, the proposed algorithm considerably decreases the

identification time, thereby making it suitable for real-time

applications and the use with large databases.

In Case 1, since only the local Euler feature is used for

indexing, the identification time is the fastest (0.043 seconds);

however the accuracy is lower compared to Daugman’s al-

gorithm. The accuracy improved when both the global and

local features are used sequentially. Further, as shown in Table

VII, Case 3 yields the best performance in terms of accuracy

(97.21%) with an average identification time of less than 2

seconds.

X. CONCLUSION

In this paper we address the challenge of improving the

performance of iris verification and identification. The paper

presents an accurate non-ideal iris segmentation using the

modified Mumford-Shah functional. Depending on the type of

abnormalities likely to be encountered during image capture,

a set of global image enhancement algorithms is concurrently

applied to the iris image. While this enhances the low quality

regions, it also adds undesirable artifacts in the original high

quality regions of the iris image. Enhancing only selected

regions of the image is extremely difficult and not pragmatic.

This paper describes a novel learning algorithm that selects

enhanced regions from each globally enhanced image and

synergistically combines to form a single composite high

quality iris image. Furthermore, we extract global texture

features and local topological features from the iris image.

The corresponding match scores are fused using the proposed

2ν-SVM match score fusion algorithm to further improve the

performance. Iris recognition algorithms require significant

amount of time to perform identification. We have proposed

an iris indexing algorithm using local and global features

to reduce the identification time without compromising the

identification accuracy. The performance is evaluated using

three non-homogeneous databases with varying characteristics.

The proposed algorithms are also compared with existing

algorithms. It is shown that the cumulative effect of accurate

segmentation, high quality iris enhancement, and intelligent

fusion of match scores obtained using global and local features

reduces the false rejection rate for verification. Moreover, the

proposed indexing algorithm significantly reduces the compu-

tational time without affecting the identification accuracy.

ACKNOWLEDGMENT

The authors would like to thank Dr. Patrick Flynn, CASIA

(China), and U.B.I. (Portugal) for providing the iris databases

used in this research. Authors also acknowledge the reviewers

and editors for providing constructive and helpful comments.

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14

TABLE VII

IRIS IDENTIFICATION PERFORMANCE WITH AND WITHOUT THE PROPOSED IRIS INDEXING ALGORITHM. ACCURACY IS REPORTED FOR RANK #1

IDENTIFICATION USING A DATABASE OF 2085 CLASSES WITH 26,881 IRIS IMAGES.

Algorithms Local feature Global feature Fusion Identification accuracy (%) Time (seconds)

Without indexing

DaugmanIriscode [2] - Texture - 95.89 5.58

(Baseline)Proposed2ν-SVM Euler code Texture 2ν-SVM 97.21 220.14

Fusion

With indexing

Proposedalgorithm Euler code - - 92.39 0.043Case 1

Proposedalgorithm Euler code Texture - 96.57 1.15

Case 2Proposedalgorithm Euler code Texture 2ν-SVM 97.21 1.82

Case 3

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Mayank Vatsa is a graduate research assistant inthe Lane Department of Computer Science andElectrical Engineering at West Virginia University.

He is currently pursuing his Doctoral degree inComputer Science. He had been actively involvedin the development of a multimodal biometric sys-

tem which includes face, fingerprint, signature, andiris recognition at Indian Institute of TechnologyKanpur, India from July 2002 to July 2004. His

current areas of interest are pattern recognition,image processing, uncertainty principles, biometric

authentication, watermarking, and information fusion. Mayank has more than

65 publications in refereed journals, book chapters, and conferences. He hasreceived four best paper awards. He is a member of the IEEE, Computer

Society, and ACM. He is also a member of Phi Kappa Phi, Tau Beta Pi,Sigma Xi, Upsilon Pi Epsilon, and Eta Kappa Nu honor societies.

Richa Singh is a graduate research assistant in

the Lane Department of Computer Science andElectrical Engineering at West Virginia University.She is currently pursuing her Doctoral degree in

Computer Science. She had been actively involved inthe development of a multimodal biometric system

which includes face, fingerprint, signature, and irisrecognition at Indian Institute of Technology Kan-pur, India from July 2002 to July 2004. Her cur-

rent areas of interest are pattern recognition, imageprocessing, machine learning, granular computing,

biometric authentication, and data fusion. Richa has more than 65 publications

in refereed journals, book chapters, and conferences, and has received fourbest paper awards. She is a member of the IEEE, Computer Society, and ACM.She is also a member of Phi Kappa Phi, Tau Beta Pi, Upsilon Pi Epsilon, and

Eta Kappa Nu honor societies.

Afzel Noore received his Ph.D. in Electrical Engi-

neering from West Virginia University. He workedas a digital design engineer at Philips India. From1996 to 2003, Dr. Noore served as the Associate

Dean for Academic Affairs and Special Assistantto the Dean in the College of Engineering andMineral Resources at West Virginia University. He

is a Professor in the Lane Department of ComputerScience and Electrical Engineering. His researchinterests include computational intelligence, biomet-

rics, software reliability modeling, machine learning,hardware description languages, and quantum computing. His research has

been funded by NASA, NSF, Westinghouse, GE, Electric Power ResearchInstitute, the US Department of Energy, and the US Department of Justice.Dr. Noore has over 85 publications in refereed journals, book chapters, and

conferences. He has received four best paper awards. Dr. Noore is a member ofthe IEEE and serves in the editorial boards of Recent Patents on Engineeringand the Open Nanoscience Journal. He is a member of Phi Kappa Phi, Sigma

Xi, Eta Kappa Nu, and Tau Beta Pi honor societies.