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

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, retina, iris, and ear) or behavioral characteristic (for example, gait, signature, voice), (Radha and Kavitha, 2011). Since biometric information is an integral part of an individual, the potential to misuse or abuse this information poses a serious threat to privacy (The Irish Council for Bioethics, 2009). While the range of body features that can be used for biometric recognition has greatly expanded since this technology was first established, not all physiological or behavioral characteristics are suitable for biometric recognition since there are some criteria used for choosing a good biometric. Based on evaluations, it is clear that there are strong and weak biometric, with the stronger biometrics meeting more of the said criteria. While no biometric modality fulfils all the criteria optimally, certain modalities satisfy more of the criteria than others (for example, fingerprint and iris would score better overall than dynamic signature and keystroke dynamics) and would, therefore, be deemed more reliable or “stronger” in terms of their suitability for recognition purposes.

1.2 Background to the Study

Biometrics is the science of automated recognition of persons based on one or multiple physical or behavioral characteristics. Among several biometrics, iris biometrics have gained lots of attention recently because it is known to be one of the best biometrics (Daugman, 1993) for recognition. The iris is visible, yet protected organ that remain unchanged throughout the life span of an individual. These features make it very desirable for use as a biometric for identifying individuals. One of the most crucial steps in building an iris security system is iris segmentation in the presence of noises such as varying pupil sizes, shadows, specular reflections and highlights. The step definitely affects the performance of the iris recognition system since the iris code is generated from the iris pattern and the pattern is affected by iris segmentation. Thus, for a secure iris recognition system, robust iris segmentation is a prerequisite. Iris segmentation is a crucial step in iris recognition since it severely affects the system‟s performance. This section of the algorithm consists in segmenting the specific iris region from an eye image by locating the exact iris boundary, the pupil region, and the upper and lower eyelids. In some cases, artifacts can be found in the resulting iris image which can be a combination of eyelash occlusion, eyelid occlusion and/or noise. Advanced algorithms are required for successful removal of these artifacts in order to generate a clean iris region for subsequent recognition. In fact, various methods have been proposed to identify and eliminate artifacts in iris images, particularly by detecting and removing eyelash occlusion and eliminating specular reflections. In general, most algorithms perform reasonably well except that they tend to overestimate eyelash occlusion (Xie ,2007).

However, it has been discovered that often the pupillary and limbic boundaries are not completely circular, which led Daugman to study alternative segmentation techniques for modeling the iris boundaries. His recent contributions to iris biometrics contains more methods to detect the iris inner and outer boundaries with active contours which leads to more embedded coordinate systems and using Fourier-based methods in order to solve iris trigonometry and projective geometry for handling off-axis gaze by rotating the eye into orthographic perspective (Daugman, 2004).

MSC Project Topics and Complete Thesis in Computer Science

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

Department: Computer Science (M.Sc Thesis)

Format: MS Word

Chapters: 1 - 5, Preliminary Pages, Abstract, References, Appendix.

No. of Pages: 75

NB: The Complete Thesis is well written and ready to use. 

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