ABSTRACT
is a physical, electronic and biological means of providing and accurate identification of people. It entails data acquisition, storage and matching at points of access or identification.
Biometrics is an emerging that identifies people by their physical and/or behavioral and practically requires the person to be identified to be physically present at the point of identification.
In this thesis, efforts to improve biometrics were made in developing an algorithm that will enhance fingerprint images for effective matching and rapid identification.
The algorithm was based on wavelet processing which employed Daubechies’ wavelets for decomposition as well as reconstruction. Wavelet smoothing and denoising were also included in the algorithm.
TABLE OF CONTENTS
TITLE PAGE ii
DEDICATION iii
ACKNOWEDGMENT iv
ABSTRACT v
LIST OF FIGURES vii
LIST OF TABLES viii
TABLE OF CONTENTS ix
CHAPTER ONE: INTRODUCTION
1.1 PREAMBLE 1
1.2 DEFINITIONS 2
1.3 BRIEF HISTORY OF BIOMETRICS 5
1.4 BIOMETRIC SYSTEMS 7
1.4.1 IMPORTANCE OF BIOMETRIC IDENTIFICATION 9
1.4.2 APPLICATIONS OF BIOMETRICS 10
CHAPTER TWO: LITERATURE REVIEW
2.1 PRELIMINARY 12
2.2 FINGERPRINT IMAGE ENHANCEMENT TECHNIQUES 13
2.3 WAVELET PROCESSING 23
2.4 IMAGE ENHANCEMENT USING WAVELET PROCESSING 32
2.5 FINGERPRINT IMAGE ENHANCEMENT USING WAVELET PROCESSING 43
2.6 SUMMARY OF WAVELET TRANSFORM 55
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 DEVELOPMENT OF FINGERPRINT IMAGE ENHANCEMENT
ALGORITHM USING WAVELET PROCESSING 58
3.2 BACKGROUND INFORMATION ON WAVELET THEORY 58
- FINGERPRINT ENHANCEMENT ALGORITHM BASED ON WAVELETS 63
- ACQUISITION OF FINGERPRINT IMAGE 67
- NORMALIZATION 67
- WAVELET DECOMPOSITION 68
- RECONSTRUCTION OF ENHANCED FINGERPRINT IMAGE 68
- WAVELET FILTERING 68
CHAPTER FOUR: EXPERIMENTS 69
- DISCUSSION OF EXPERIMENTS AND PROCEDURE 69
- QUANTIFICATION OF THE DEGREE OF ENHANCEMENT 70
- ANALYSIS OF RESULTS OF FINGERPRINT IMAGE ENHANCEMENT ALGORITHM BASED ON WAVELET PROCESSING 73
- COMMENTS ON IMPROVEMENTS BY WAVELET PROCESSING 74
- SPECIFIC APPLICATION AREAS FOR DEPLOYMENT OF THE ALGORITHM 74
- LIMITATIONS OF BIOMETRIC SYSTEMS 75
CHAPTER FIVE:77
5.1 RECOMMENDATIONS77
5.2 CONCLUSION78
References:79
Appendix
INTRODUCTION
1.1 Preamble
Today’s world has been greatly modified by technology in the efforts of man to deal with his environment in order to tame it to suit his needs; with particular reference to electronics.
Virtually, all aspects of our modern civilization depend on electronic [including hardware and software] systems for smooth and effective operations of daily activities.
Ranging from military, communications, government, education, banking, automobiles and manufacturing to the space industry; all rely on electronic systems for various applications like process automation, control and monitoring systems,
security, automated parking control, 24 hours surveillance systems, traffic collision avoidance system (TCAS) of aircrafts and on board data handling subsystems of satellites (OBDH).
With the dynamic growth of the world’s population, global economic meltdown, food scarcity, and unemployment; all these factors have led to significant increase in violent crimes globally making security a great challenge to all and sundry.
Governments, business owners, the organized private sector and educational institutions all require a robust, rugged and dependable means of recognizing and verifying peoples’ identities at various points of access to specific areas physically and electronically.
In order to ensure that intruders, impersonators are promptly fished out prior to breaching the security of these valued resources, information, assets and vaults; a robust and effective means of identifying and verifying peoples’ identities is paramount. The search for such systems culminated recently in biometrics.
REFERENCES
Evans, “Criminal Investigations: Crime Scene Investigation”, J. L. French (Ed), InfoBase Publishing, New York, 2009, pp 13-29.
Ribaric and I. Fratic, “A Biometric Identification System Based on EigenPalm and EigenFinger Features”, IEEE transactions on Pattern Analysis and Machine Intelligence, Vol. 27, № 11, November 2005, p. 1698.
Ma, Y. Hong, and T. Tan, “Iris Recognition Using Circular Symmetric Filters”, IEEE Proceedings, 16th Int. Conf. on Pattern Recognition, 2002, pp. 414-417.
Cappelli, A. Lumini, D. Maio and D. Maltoni, “Fingerprint Image Reconstruction from Standard Templates”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, № 9, September 2007, p. 1489.
Bansal, P. Arora, M. Gaur, P. Sehgal, and P. Bedi, “Fingerprint Image Enhancement Using Type-2 Fuzzy Sets”, IEEE Sixth Int. Conf. on Fuzzy Systems and Knowledge Discovery, 2009, pp 412-417.
StudentsandScholarship Team
Be the first to comment