Fingerprint recognition using matlab
Table Of Contents
Acknowledgement – – – .. 2
Chapter ONE
: Introduction – – – – 61.1 INTRODUCTION – – . 7
1.2 Biometrics – – – – 8
1.3 Biometrics Authentication Techniques – . 8
1.4 How Biometric Technologies Work – – . 8
1.4.1 Enrollment – – .. 9
1.4.2 Verification – – – – .. 10
1.4.3 Identification – – – – .. 10
1.4.4 Matches Are Based on Threshold Settings – .. 11
1.5 Leading Biometric Technologies – – – 12
1.6 Fingerprints as a Biometric – – – – .. 13
1.6.1 Fingerprint Representation – – – . 14
1.6.2 Minutiae – – – 14
Chapter TWO
: Motivation for the project – – 16
2.1 Problem Definition – – – – 17
2.2 Motivation for the Project – – 18
2.3 About the Project – – – – .. 18
Chapter THREE
: System design – – . 20
2.1 System Level Design – – – . 21
2.2 Algorithm Level Design – – .. 22
Chapter FOUR
: Fingerprint image preprocessing – – – . 24
4.1 Fingerprint Image Enhancement – – .. 25
4.1.1 Histogram Equalization: – – – – 26
4.1.2 Fingerprint Enhancement by Fourier Transform – – .. 28
4.2 Fingerprint Image Binarization – – – .. 30
4.3 Fingerprint Image Segmentation (orientation flow estimate) – – – – .. 32
4.3.1 Block direction estimation – – – .. 32
3.3.2 ROI extraction by Morphological operation – .. 34
Chapter FIVE
: Minutiae extraction – – – – . 36
5.1 Fingerprint Ridge Thinning – – – – .. 37
5.2 Minutia Marking – – – – 39
Chapter six: Minutiae post-processing – – – 42
False Minutia Removal – – – .. 43
Chapter seven: Minutiae match – – . 46
6.1 Alignment Stage – – – – 48
6.2 Match Stage – – – . 50
Chapter eight: System evaluation and conclusion – – 51
8.1 Evaluation of the system – – .. 52
8.2 Conclusion – – – . 54
Appendix – – – 55
REFERENCES – – – – . 74
Thesis Abstract
Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this project is to develop a complete system for fingerprint verification through extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of image enhancement and binarization is first applied on fingerprints before they are evaluated. Many methods have been combined to build a minutia extractor and a minutia matcher. Minutia-marking with false minutiae removal methods are used in the work. An alignment-based elastic matching algorithm has been developed for minutia matching. This algorithm is capable of finding the correspondences between input minutia pattern and the stored template minutia pattern without resorting to exhaustive search. Performance of the developed system is then evaluated on a database with fingerprints from different people.
Thesis Overview
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