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Title of Thesis

Iris Localization Using Grayscale Texture Analysis And Recognition Using Bit Planes

Author(s)

Abdul Basit

Institute/University/Department Details
Department of Computer Engineering, College of Electrical and Mechanical Engineering / National University of Sciences and Technology, Rawalpindi
Session
2009
Subject
Computer Engineering
Number of Pages
183
Keywords (Extracted from title, table of contents and abstract of thesis)
Iris, Localization, Grayscale, Texture, Analysis, Recognition, Bit, Planes, verification, human, beings, security

Abstract
Identification and verification of human beings is very important because of today’s security condition throughout the world. From the beginning of 19th century, iris is being used for recognition of humans. Recent efforts in computer vision have made it possible to develop automated systems that can recognize individuals efficiently and with high accuracy. The main functional components of existing iris-recognition systems consist of image acquisition, iris localization, feature extraction and matching. While designing the system, one must understand physical nature of the iris, image processing and their analysis to make an accurate system. The most difficult and time consuming part of iris recognition is iris localization. In this thesis, performance of iris localization and normalization processes in iris recognition systems has been enhanced through development of effective and efficient strategies. Bit plane and wavelet based features has been analyzed for recognition.
Iris localization is the most important step in iris recognition systems. Iris is localized by first finding the boundary between pupil and iris using different methods for different databases. This is because the iris image acquiring devices and environment is different. Non-circular boundary of pupil is obtained by dividing the circular pupil into specific points and then these points are forced to shift at exact boundary position of pupil which are linearly joined.
The boundary between iris and sclera is obtained by finding points of maximum gradient in radially outwards different directions. Redundant points are discarded by finding certain distance from the center of the pupil to the concerned relevant point. This is because the distance between center of pupil and center of iris is very small. The domain for different directions is left and right sectors of iris when pupil center is at the origin of the axes.
Eyelids are detected by fitting parabolas using points satisfying specific criterions. Experimental results show that the efficiency of the proposed method is very high as compared to other existing methods.
Improved localization results are reported using proposed methods. The experiments are carried out for four different iris image datasets. Correct localization rate of 100% (pupil circular boundary), 99.8% (non-circular pupil), 99.77% (iris outer-iiiboundary), 98.91% (upper eyelid detection) and 96.6% (lower eyelid detection) has been achieved for different datasets.
To compensate the change in size of the iris due to pupil constriction / dilation and camera to eye distance, different normalization schemes have been designed and implemented based on difference reference points.
Mainly two different features extraction methodologies have been proposed. One is related to the bit planes of normalized image and other utilizes the properties of wavelet transform.
Recognition results based on bit plane features of the iris have also been obtained and correct recognition rate of up to 99.64% has been achieved using CASIA version 3.0. Results on other databases have also provided encouraging performance with accuracy of 94.11%, 97.55% and 99.6% on MMU, CASIA version 1.0 and BATH iris databases respectively.
Different wavelets have been applied to get best iris recognition results. Different levels of wavelet transforms (Haar, Daubechies, Symlet, Coiflet, Biorthogonal and Mexican hat) along with different number of coefficients have been used. Coiflet wavelet resulted in high accuracies of 99.83%, 96.59%, 98.44% and 100% on CASIA version 1.0, CASIA version 3.0, MMU and BATH iris databases respectively.

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5,257 KB
S. No. Chapter Title of the Chapters Page Size (KB)
1 0 CONTENTS

 

vi
95 KB
2

1

INTRODUCTION

1.1 Biometrics

1.2 Some Biometrics
1.3 Location of Iris in Human Eye

1.4 Research on Iris Recognition

1.5 Iris Recognition System

1
453 KB
3 2 EXISTING IRIS RECOGNITION TECHNIQUES

2.1 Background
2.2 Iris Image Acquisition
2.3 Iris Localization
2.4 Iris Normalization
2.5 Feature Extraction
2.6 Matching Algorithms

11
180 KB
4 3 PROPOSED METHODOLOGIES

3.1 Proposed Iris Localization Method

3.2 Proposed Normalization Methods
3.3 Proposed Feature Extraction Methods

3.4 Matching

27
305 KB
5 4 DESIGN & IMPLEMENTATION DETAILS

4.1 Iris Localization
4.2 Normalization Methods
4.3 Feature Extraction Methods
4.4 Matching

50
1,896 KB
6

5

RESULTS & DISCUSSIONS

5.1 Databases Used for Evaluation
5.2 CASIA Version 1.0
5.3 CASIA Version 3.0
5.4 University of Bath Iris Database (free version)
5.5 MMU Version 1.0
5.6 Errors in Localization
5.7 Comparison with Other Methods
5.8 Normalization
5.9 Feature Extraction and Matching

79
2,716 KB
7

6

CONCLUSIONS AND FUTURE RESEARCH WORK

6.1 Design & Implementation Methodologies
6.2 Performance of the Developed System
6.3 Future Research Work

141
100 KB
8 7 APPENDICES & REFERENCES

145


152 KB