PALM PRINT RECOGNITION SYSTEM

TABLE OF CONTENTS

Title page…………………………………………………………………………………………

Declaration……………………………………………………………………………………….

Certification……………………………………………………………………………………..

Dedication……………………………………………………………………………………….

Acknowledgement………………………………………………………………………………

Abstract………………………………………………………………………………………….

Table of content………………………………………………………………………………..

List of tables…………………………………………………………………………………..

List of figures……………………………………………………………………………………

CHAPTER ONE: INTRODUCTION…………………………………………………………

1.1 BACKGROUND OF STUDY……………………………………………………………….

1.2 PROBLEM STATEMENT………………………………………………………………….

1.3 SCOPE OF STUDY…………………………………………………………………………

1.4 AIMS AND OBJECTIVES…………………………………………………………………

1.5 SIGNIFICANCE OF STUDY………………………………………………………………

1.6 PROJECT LAYOUT………………………………………………………………………..

CHAPTER TWO: LITERATURE REVIEW……………………………………………….

2.1 PREABLE……………………………………………………………………………………

2.2 BIOMETRICS……………………………………………………………………………….

2.1.1CONCEPT OF PALMPRINT……………………………………………………

2.2.2PALM IDENTIFICATION……………………………………………………

2.3.3PALM RECOGNITION TECHNIQUE………………………………………

2.4.4HARDWARE……………………………………………………………………

2.5.5SOFTWARE……………………………………………………………………

CHAPTER THREE: RELATED WORK EXISTING SYSTEM………………………

3.1 ANALYSIS OF THE EXISTINGSYSTEM………………………………………………….

3.2 PROBLEM OF THE EXISTINGSYSTEM…………………………………………………..

3.3 PROPOSEDSYSTEM……………………………………………………………………

3.4 PROPOSED SYSTEM DESIGN…………………………………………………………

3.5 CHOICE OF PROGRAMMING TOOL…………………………………………………

CHAPTER FOUR: IMPLEMENTATION AND RESULT EVALUATION…………

4.1 DATA STRUCTURE…………………………………………………………………………

4.1.1 EXPERIMENT SETUP……………………………………………………………………

4.1.2 DATABASE SETTINGS………………………………………………………………….

4.2 USER INTERFACE………………………………………………………………………

4.3 INPUT DESIGN………………………………………………………………………………

4.4 OUTPUT DESIGN……………………………………………………………………………

4.5 CLASSIFICATION ACCURACY…………………………………………………………

CHAPTER FIVE: SUMMARY AND CONCLUSION ……………………………………

5.1 SUMMARY…………………………………………………………………………………

5.2 CONCLUSION………………………………………………………………………………

5.3 FUTURE WORK…………………………………………………………………………….

REFERENCES…………………………………………………………………………………..

APPENDIX…………………………………………………………………………………….

LIST OF FIGURE

3.1 proposed system design…………………………………………..

4.1Matlab work environment…………………………………………

4.2 User interface……………………………………………………..

4.3 Loading of the database……………………………………………

4.4 Pre-processing and normalization…………………………………

4.5 Database loaded and ready to be trained………………………….

4.6 Images trained using 2D-PCA…………………………………….

4.7 Palm print indicating recognition and time taken…………………

4.8 Image of a palm………………………………

4.9 Image of another tested been recognized………………………….

4.10 Image of an unrecognized palm print……………………………

4.11 Image of a mismatched palm print………………………………

LIST OF TABLE

4.8 PCA classification Accuracy……………………………………….

LIST OF APPENDIX

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Palm print recognition is one of the bio-metrics available at the present. Bio metric systems are used two main categories ‘physiological’ and/or ‘behavioral’. The physiological category includes the physical human traits such as palm print, hand shape, eyes, veins, etc. The behavioral category includes the movement of the human, such as hand gesture, speaking style, signature (Jain, Bolle, & Pankanti 1999).

Palm prints are stable and show high accuracy in representing each individual’s identity. (Campbell, 2000) They have been commonly used in law enforcement and forensic environments. Since the surface of the palm print is larger than the fingerprint, a higher quantity of identifying features can be extracted from the palm print. Moreover, users consider hand biometrics as being user friendly, easy to use, and convenient. Palm print acquisition is based on standard charge-coupled device (CCD)-based optical scanning (Renold, 2010).

Palm print based biometric approaches have been intensively developed over a decade because they possess several advantages over other systems. Palm print images can be acquired with low resolution cameras and scanners and still have enough information to achieve good recognition rates. If high resolution images are captured, ridges and wrinkles can be detected (Jain, & Pankanti, 201). Forensic applications typically require high resolution imaging, with at least 500 dpi.

The palmprint is a relatively new biometric feature, has several advantages compared with currently available features (Maltoni, et al, 2004). The seven factors affect the determination of a biometric identifier in a particular application: universality, uniqueness, Permanence, collectability, performance, acceptability and circumvention. Palm print recognition has been introduced a decade ago. It has gradually attracted the attention of various researchers due to its richness in amount of features. Palm is the inner surface of the hand between the wrist and the fingers.

Palm print recognition has been introduced a decade ago. Palm is the inner surface of the hand between the wrist and the fingers. The Palm area contains a large number of features that can be used as biometric features such as Principal lines, geometry, wrinkle, delta point, minutiae, datum point features and texture. The principle lines are also called as flexion creases. The formation of these lines is related to the finger movements, tissue structures and the purpose of skin. Even the palm prints of identical twins are different (Biggun and Graland, 1987).

The measurement of these traits helps in authentication using the biometric systems. One of the most successful biometric systems is the palm print recognition system. This system recognizes on the basis of the palm print of a person. The interesting part is that the ridge structure is permanent. This ridge structure is formed at about the thirteenth week of the embryonic development. This formation gets completed by the eighteenth week. The palm print recognition system has advantages over the other physiological biometric systems. Some of the advantages are fixed line structure, low intrusiveness, low cost capturing device, low resolution imaging. Thus palmprint recognition is a very interesting research area. A lot of work has already been done in this area, but there is still a lot of scope to make the systems more efficient. Here, we have tried to analyze the already existing systems and thereby propose a new approach.

Palmprint recognition techniques have been grouped into two main categories, first approach is based on low-resolution features and second approach is based on high-resolution features. First approach make use of low-resolution images (such as 75 or 150 ppi), where only principal lines, wrinkles, and texture are extracted. Second approach uses high resolution images (such as 450 or 500 ppi), where in addition to principal lines and wrinkles, more discriminant features like ridges, singular points, and minutiae can be extracted (Brunelli & Poggio, 1993).

1.2 Problem Statement

There is a need for modern technology to use systems that recognize or verify the identity of people when performing task or transactions. Passwords or token suffer from loss or stolen problems. Thus, there is a need to develop more usable and secure system. The answer to this is using biometric systems.

The bio-metric systems that are used for commercial applications or forensic applications depend on many factors such as, real-time processing, high accuracy, low complexity, low cost and design simplicity. The palm print recognition systems which are used for commercial applications require features such as principal lines and wrinkles which extracted from low resolution images. Workers and old people may not provide clear physiological features such as fingerprints or voice because of their problematic skin caused by physical work. Recently, voice, face, and iris-based verification’s have been studied extensively. The development of multi scale image transforms provides the bio-metric systems with transformations which deal with low resolution images to identify the individuals from their palm prints. The combination between multi scale image transform together with 2 D projection technique and back-propagation neural network will be used in this research.