REDUCING THE EFFECT OF PARTIAL OCCLUSIONS

ON IRIS RECOGNITION

Meryem Erbilek and Önsen Toygar

Computer Engineering Department, Eastern Mediterranean University

Gazimağusa, Mersin 10, Turkey

Keywords: Biometrics, iris recognition, feature extraction, multiple classifier systems, Principal Component Analysis.

Abstract: The difficulty in the process of human identification by iris recognition is that the iris images captured may

have occlusions by the eyelids and eyelashes. In that case, recognition of occluded iris patterns becomes

hard and the corresponding person may not be correctly recognized. In order to reduce the effect of eyelid or

eyelash occlusion on the recognition of human beings by their iris patterns, we propose a simple and

efficient method for iris recognition using specific regions on the iris images without using the traditional

preprocessing approach before applying the feature extraction method to recognize the irises. First of all,

these regions are individually experimented and then the outputs of each region are combined using a

multiple classifier combination method with the feature extraction method Principal Component Analysis

(PCA). The experiments on the iris images, with and without occlusions, demonstrate that the proposed

approach achieves better recognition rates compared to the recognition rates of the holistic approaches.

1 INTRODUCTION

Reliable person identification or verification is

becoming a very important topic in recent years in

most of the countries in the world. Biometrics is a

popular field of study which is related to the

recognition of individuals based on their

physiological and/or behavioral characteristics. Iris

recognition is one of the most secure and reliable

biometric recognition systems (Ma et al., 2004; Cui

et al., 2004; Daugman, 2006) to recognize human

beings. It is not suitable to get iris images of people

in each condition and enviroment to recognize

human since the iris images must be captured by a

special camera from people who are willing to do

this. However, for controlled enviroments such as

airports, iris recognition seems to be a convenient

biometrics to identify human beings.

The difficulty in the process of human

identification by iris recognition is that the iris

images captured may have occlusions by the eyelids

and eyelashes. In this respect, recognition of

occluded iris patterns becomes hard and the

corresponding person may not be correctly

recognized.

In order to reduce the effect of eyelid or eyelash

occlusion on the recognition of human beings by

their iris patterns, we propose a simple and efficient

method for iris recognition using specific regions on

the iris images without using the traditional

preprocessing approach before applying the feature

extraction methods to recognize the irises. In our

method, the iris image is partitioned into vertical left

and right regions and also top and bottom regions of

the iris are partitioned horizontally into two regions.

In this way, the horizontal or vertical regions of the

iris pattern without including the pupil is obtained

from the iris image. Firstly, these regions are

individually experimented on the CASIA iris dataset

and the recognition rate of each region is recorded

using Principal Component Analysis (PCA) as the

feature extraction method. Then, an appropriate

multiple classifier combination method is used to

combine the output of these regions. Various

experiments are performed to test the recognition

performance of the classical and the proposed

approaches using iris images with and without

occlusions on different regions of the irises. The

results are presented in the further sections.

The rest of the paper is organized as follows.

Section 2 describes PCA which is the feature

extraction method used in this study. The proposed

approach for iris recognition based on the

partitioning method is described in Section 3. The

543

Erbilek M. and Toygar Ö. (2008).

REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 543-546

DOI: 10.5220/0001084305430546

 SciTePress

experimental study and the discussions are presented

in Section 4. Finally, Section 5 concludes the paper.

2 FEATURE EXTRACTION

METHOD

Appropriate feature extraction is an essential

component of a successful biometrics recognition

algorithm. For this purpose, statistical

dimensionality reduction methods such as Principal

Component Analysis, Linear Discriminant Analysis

and Independent Component Analysis are

demonstrated to be successful in several academic

studies and commercial applications (Toygar and

Acan, 2004, Chu and Chen, 2005). The success and

popularity of these algorithms are mainly due to

their statistics–based ability of automatically

deriving the features instead of relying on humans

for their definitions. These algorithms are widely

studied for the recognition of human beings using

their physiological characteristics such as the face,

iris, fingerprint and so on.

In this study, we apply PCA which is one of the

most popular methods used in the literature for

feature extraction on different regions of the iris

images. PCA projects images into a subspace such

that the first orthogonal dimension of this subspace

captures the greatest amount of variance among the

images and the last dimension of this subspace

captures the least amount of variance among the

images (Kirby and Sirovich, 1990; Turk and

Pentland, 1991). In this respect, the eigenvectors of

the covariance matrix are computed which

correspond to the directions of the principal

components of the original data and their statistical

significance is given by their corresponding

eigenvalues.

3 PARTITIONING APPROACH

ON IRIS RECOGNITION

Partitioning approach for the iris recognition

problem is applied by dividing the iris images into

four segments and the features of each segment are

extracted independent of each other using PCA

method.

All the training and testing face images used in

the experimental study are cropped as shown in

Fig.1. Cropping operation is applied in the same way

for both the training and test images, so that all the

images include only the iris and the pupil after this

operation. Partitioning approach is applied after this

operation and divides the image into four segments,

each segment including the horizontal top,

horizontal bottom, vertical left and vertical right

regions of the iris excluding the pupil from the iris

as shown in Fig.1. In this way, the iris patterns of the

training and test images are stored in four different

partitions or segments.

Figure 1: Partitions of an iris image: left, right, top and

bottom regions.

After applying PCA algorithm on each segment

of training and test images, Euclidean distance

measure is used to find the distance between features

of these image segments. For each test image, the

distances between the test image and all the training

images are compared. The training image that has

the minimum distance to the test image is the image

that mostly resembles to it.

In this study, PCA is implemented with a divide-

and-conquer strategy for the solution of the iris

recognition problem. The divide-and-conquer

approach implemented over multiple classifier

systems (MCSs) (Ho et al.,1994; Kittler et al.,1998)

is used to improve the computational efficiency and

recognition performance of PCA method on the iris

recognition problem. MCSs combine the output

information provided by two or more classifiers.

In the implementation of the divide-and-conquer

methodology for the iris recognition problem, iris

images are divided into two horizontal (top and

bottom regions) and two vertical (left and right

regions) segments and PCA is applied on each iris

segment as a feature extraction method.

Consequently, a multiple classifier system

is established based on a particular distance

measure. Finally the outputs of multiple classifiers

are combined using a well-known multiple classifier

combination method to recognize the whole iris

under occlusions.

4 EXPERIMENTAL RESULTS

The experiments were done on the iris images

chosen from the third version of CASIA iris dataset

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

544

(CASIA-IrisV3). The performance of the proposed

approaches are tested using PCA algorithm applied

on the individual regions of the iris images. PCA

approach is applied in the same way as explained in

(Toygar and Acan, 2004). In this study, the iris

images used are cropped so that they only include

the iris and pupil regions. The iris images were

scaled down to 199x156 pixels from the original size

of 640x480 pixels. The experiments were done using

100 training and 100 test images (two samples per

person).

In the implementation of the proposed

approach, multiple classifiers are considered using

the Majority Voting classifier combination method.

The output of each individual classifier is computed

separately, followed by the multiple-classifier

combination procedure which produces the final

classifier or recognition output. Majority Voting

method selects the class which receives the largest

number of votes as the majority decision (Kittler et

al.,1998). In case of equality of the votes, one of the

classifiers with maximum number of votes is

selected arbitrarily.

Four set of experiments are performed and the

results are presented in Table 1 through Table 4. The

recognition rates of the holistic PCA (PCA applied

on the whole iris image), partitioned PCA (PCA

applied on the four regions of the iris image

individually) and the proposed approach

(combination of the four regions using a MCS) are

recorded. The results are presented using the images

without occlusions and the images with occlusions

on the training and test images.

In the first set of experiments, the whole iris

images are experimented using the holistic PCA

method, partitioned PCA on the left, right, top and

bottom regions individually and the proposed

approach to combine the outputs of each individual

region of the iris image.

As shown in Table 1, the recognition rate using

the holistic PCA method is 84% and the three

partitioned PCA methods achieves less than this

recognition rate while partitioned PCA applied on

the bottom region of iris achieves 88% which is

better than the holistic counterpart. Additionally, the

proposed approach combines outputs of the

individual regions and the recognition rate is

Table 1: Recognition rates on iris images without

occlusion.

Approach Recognition Rate (%)

Holistic PCA (Whole iris) 84

Partitioned PCA (Left) 80

Partitioned PCA (Right) 66

Partitioned PCA (Top) 78

Partitioned PCA (Bottom) 88

Proposed Approach 96

Table 2: Recognition rates on iris images with occlusions

on top of the images.

Recognition Rate (%)

using occlusion of

Approach

3% 5% 7% 10%

Holistic PCA (Whole iris) 70 80 70 66

Partitioned PCA (Left) 76 70 70 70

Partitioned PCA (Right) 76 78 82 84

Partitioned PCA (Top) 56 46 50 44

Partitioned PCA (Bottom) 54 40 40 32

Proposed Approach 80 82 76 70

Table 3: Recognition rates on iris images with occlusions

on bottom of the images.

Recognition Rate (%)

using occlusion of

Approach

3% 5% 7% 10%

Holistic PCA (Whole iris) 82 80 72 60

Partitioned PCA (Left) 80 80 66 66

Partitioned PCA (Right) 78 80 84 72

Partitioned PCA (Top) 68 62 44 38

Partitioned PCA (Bottom) 64 48 48 48

Proposed Approach 92 84 80 68

Table 4: Recognition rates on iris images with occlusions

on top and bottom of the images.

Recognition Rate (%)

using occlusion of

Approach

3% 5% 7% 10%

Holistic PCA (Whole iris) 74 72 62 52

Partitioned PCA (Left) 70 68 60 62

Partitioned PCA (Right) 80 76 78 68

Partitioned PCA (Top) 40 36 32 14

Partitioned PCA (Bottom) 36 42 24 22

Proposed Approach 74 70 68 64

increased to 96% which is a significant

improvement.

On the other hand, the iris images are occluded

artificially with different percentages (3%, 5%, 7%,

10%) on different regions of the iris such as the top,

bottom and both of them. As an example, 10%

occlusions on top, bottom, and top and bottom

regions are shown in Figure 2.

REDUCING THE EFFECT OF PARTIAL OCCLUSIONS ON IRIS RECOGNITION

545

Figure 2: Iris images (a) without occlusions and with

occlusions on (b) top (c) bottom and (d) top and bottom

regions.

In the second set of experiments, the iris images

are occluded from the top region of the iris. The

recognition rates for various occlusion percentages

are presented in Table 2. The data on this table

shows that some of the partitioned PCA approaches

slightly increase the performance of the holistic

PCA. The recognition rate of the proposed approach

is significantly increased compared to holistic

counterparts.

The third and the fourth set of experiments also

demonstrate that the proposed approach by

combining the outputs of individual regions achieves

better performance compared to the performance of

the holistic PCA and partitioned PCA methods.

In general, the experiments using holistic PCA,

partitioned PCA and the proposed approach without

occlusions demonstrate that the proposed approach

achieves a significant improvement for the

recognition of iris images (from 84% to 96%). On

the other hand, for the iris images with occlusions on

either top or bottom of the iris, the recognition rates

demonstrate that the proposed approach is not

sensitive up to 5% occlusions. The results obtained

by the proposed approach with 5% occlusions on

either top or bottom of the irises are equivalent to

the results obtained by holistic PCA without

occlusions.

5 CONCLUSIONS

A simple and efficient approach for reducing the

effect of partial occlusions and improving the

recognition performance of the iris images is

presented. The experiments performed using holistic

PCA, partitioned PCA and the proposed approach

without occlusions demonstrate that the proposed

approach achieves a significant improvement for the

recognition of iris images. It can also be stated that

the proposed approach is not sensitive up to 5%

occlusions on top or bottom regions of the iris

images compared to the holistic PCA method

without occlusions.

ACKNOWLEDGEMENTS

Portions of the research in this paper use CASIA

IrisV3 collected by the Chinese Academy of

Sciences’ Institute of Automation (CASIA).

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