ABSTACT
Palmprint recognition has been investigated over ten year years. Palmprint is proven to be distinguishable from other features because of number of attributes. These attributes include color, clarity, position, continuity, length and variation in thickness. Lines are represented in a very efficient way and it needs low storage and consistency in detection and these are efficient for shape matching involving large database. But there will always be a problem of missing or broken lines during the extraction process of palmprint which causes difficulty in the matching process. Therefore, to eliminate this problem there is a need for efficient technique in order to reduce the number of repeated lines or broken lines in the binary images. This project proposed the use of 2-Dimensional Principal component analysis for palm print recognition. The proposed work was implemented using matlab (R2015a) software. Eigen vector was used to classify the data set obtained from 2D PCA and accuracy of 90% was obtained.
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……………………………………….
