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Author: inn ann ni
Enny - Indasyah
Thesis Title: Combining Local and Global Feature Matching for Fingerprint Recognition
Combining Local and Global Feature Matching for Fingerprint Recognition
Advisor: 洪西進
Shi-Jinn Horng
Committee: 古鴻炎
Hung-Yan Gu
Kuo-Liang Chung
Degree: 碩士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2015
Graduation Academic Year: 103
Language: 英文
Pages: 60
Keywords (in other languages): minutiae features, core point
Reference times: Clicks: 297Downloads: 5
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Fingerprint recognition is one of the biometric techniques that are used for identification purpose. Human fingerprints are rich in details called minutiae, whic can be used as identification marks for fingerprint identification. The goal of this project is to develop a complete system for fingerprint recognition througn 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. This research presents a combination of global( core point detection) and local minutiae ( termination and bifurcation) method. The algorithm uses the distance between the minutiae and core points to determine the pattern matching scores for fingerprint images. Experiments were conducted using fingerprint database comprising seven database with different sources and qualities. False Acceptance Rate (FAR) False Rejected Rate (FRR), Genuine Acceptance Rate (GAR) and Genuine Rejected Rate (GRR) were the statistics generated for testing and measuring the performance of the proposed algorithm. The experiment result from DB1_B FVC2002 perform better results than other database.The accuracy of DB1_B is 99,6%.

Declarationi Acknowledgementii Abstractiii Table of Contentsiv List of Figuresvi List of Tablesvii Chapter 1 Introduction8 1.1 Introduction9 1.2 Biomtrics10 1.3 Biomtrics Authentication Techniques10 1.4 How Biometric Technologies Work11 1.5 Leading Biometric Technologies14 1.6 Fingerprints15 1.7 Fingerprints Recognition15 1.8 Fingerprints as a Biometric16 1.9 Motivation for the project17 1.10 About the Project17 1.11 Thesis Organization18 Chapter 2 Basic Theory 19 2.1 Sensor for enrollment database20 2.2 Feature Extraction22 2.2.1 Singularity and core detection25 2.2.2 Fingerprint Enhancement by Fourier Transform27 2.2.3 Fingerprint Image Binarization28 2.2.4 Fingerprint Ridge Thinning29 2.2.5 Minutia Marking29 2.2.6 False Minutia Removal30 2.3 Minutiae Represntation32 2.4 Registration32 2.5 Evaluate performance35 Chapter 3 System Design36 3.1 System Level Design37 3.2 Algorithm Level Design37 Chapter 4 Experimental Result39 4.1 Database40 3.2 Performance evaluation index40 Chapter 5 Conclusion and Future Work52 5.1 Conclusion53 5.2 Future Work53 References55 Appendix56

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