Study of pore extraction algorithms for high resolution fingerprint recognition

Pao Yue-kong Library Electronic Theses Database

Study of pore extraction algorithms for high resolution fingerprint recognition

 

Author: Xu, Yan
Title: Study of pore extraction algorithms for high resolution fingerprint recognition
Degree: M.Sc.
Year: 2012
Subject: Fingerprints -- Identification -- Data processing.
Pattern recognition systems.
High resolution imaging.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: vii, 55 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2473661
URI: http://theses.lib.polyu.edu.hk/handle/200/6417
Abstract: Fingerprint recognition is widely used in many domains and has matured as a field of study. Although, there are three levels of fingerprint features. Most of the existing automatic fingerprint recognition systems (AFRS) are based on level-2 features (minutiae). In some situations, however, using only level-2 features cannot accurately recognize the fingerprint and level-3 features are combined with level-2 features (pores) to improve recognition. However, pore-based fingerprint recognition has a major limitation in that it requires high resolution and good quality fingerprint images. Thanks to the development of high resolution technique pore-based fingerprint recognition can now be implemented with greater success. This thesis introduces adaptive fingerprint pore extraction algorithm. It first partitions blocks as well-defined, ill-defined and background blocks based on ridge orientation and ridge frequency. Well-defined blocks are applied in the adaptive fingerprint pore module and ill-defined blocks are used in the DoG module to generate a matched filter through which pores can be detected. Then the direct pore matching algorithm is used to finish pore matching. There are two key steps in this process: first, descriptors are used to calculate the initial pore correspondence based on the similarity of two pores; second, it refines and fixes the errors of matched pores by using the RANSAC (RANdom SAmple Consensus) algorithm according to the initial pore correspondence. After finishing the mentioned two steps, the match score can be calculated. The last part depicts feature fusion. Experimental results indicate that combining pores and minutiae to recognize fingerprints is more accurate than only using pores or minutiae.

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