PERFORMANCE EVALUATION OF TWO HIGH PROFILE SEGMENTATION ALGORITHMS IN IRIS RECOGNITION SYSTEM

TABLE OF CONTENTS
ABSTRACT
TABLES OF CONTENTS

CHAPTER ONE
GENERAL INTRODUCTION
1.1       Introduction
1.2       Background to the Study
1.3       Problem Definition
1.4       Motivation for the Thesis
1.5       Aim
1.6       Objectives of the Research
1.7       Research Methodology
1.8       Contribution to Knowledge
1.9       Organization of the Rest of the Thesis

CHAPTER TWO
LITERATURE REVIEW
2.1       Introduction
2.2       Related Works
2.2       Iris Localization and Segmentation
2.3       Biometrics Technology
2.4       Expectations from Biometrics Technology

CHAPTER THREE
METHODOLOGY FOR SEGMENTATION COPMARISON
3.1       Introduction
3.2       Tools
3.3       System Architecture
3.4       Framework Data Flow
3.5       Intra and Inter Class Matching
3.6       False Acceptance Rate (FAR) and False Rejection Rate (FRR)
3.7       Threshold

CHAPTER FOUR
IMPLEMENTATION, RESULTS AND DISCUSSION
4.1       Implementation
4.2       Result and Discussion
4.3       Conclusion

CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1       Summary
5.2       Conclusion
5.3       Recommendation
REFERENCES
APPENDIX


ABSTRACT
Iris is an effective biometric application of an individual for security-related applications. However, the iris segmentation process is challenging due to the presence of eye lashes that occlude the iris. Iris segmentation is an important phase in the whole iris recognition system, for it determines the accuracy of matching. To find a fast, effective and exact iris segmentation algorithm is the key step of iris recognition. This thesis compares the two high profile segmentation algorithms of Daugman (integro-differential) and Wildes (circular hough). Simulating the two algorithms with MATLAB 2012 and image datasets from Chinese Academy of Science Institute of Automation (CASIA) version 4, the evaluation of the algorithms was carried out using the performance metric False Acceptance Rate (FAR), False Rejection Rate (FRR) and Recognition Accuracy. The analysis of the result, indicated that the Circular Hough is more accurate than intego-differential as it shows higher recognition accuracy and lower error rate.


CHAPTER ONE
GENERAL INTRODUCTION
1.1              Introduction
In today‟s information technology world, security of systems is becoming more and more important. The number of systems that have been compromised is ever increasing and authentication plays a major role as a first line of defense against intruders. The three main types of authentication are something you know (such as a password), something you have (such as a card or token), and something you are (biometric) (Penny, 2002). Passwords are notorious for being weak and easily crackable due to human nature and our tendency to make passwords easy to remember. Cards and tokens can be presented by anyone and although the token or card is recognizable, there is no way of knowing if the person presenting the card is the actual owner. Biometrics, on the other hand, provides a secure method of authentication and identification, as they are difficult to replicate, forge or steal (Penny, 2002)

Since authentication of users is essential and difficult to achieve in all systems. Shared secrets like Personal Identification Numbers (PIN) or Passwords and key devices such as Smart cards are not presently sufficient in few situations. The biometric improves the capability to recognize the person base on some characteristics possess by such individual. Biometrics is derived from a Greek word “bio” meaning life and “metrics” meaning measure to. In other word, biometrics means a measure to life.

A biometric identification system is an automatic recognition system that recognizes a person based on his or her physiological characteristic (for example, fingerprints, face.....

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Item Type: Project Material  |  Attribute: 67 pages  |  Chapters: 1-5
Format: MS Word  |  Price: N3,000  |  Delivery: Within 30Mins.
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