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Proceedings Paper

A fingerprint classification algorithm based on combination of local and global information
Author(s): Chongjin Liu; Xiang Fu; Junjie Bian; Jufu Feng
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Paper Abstract

Fingerprint recognition is one of the most important technologies in biometric identification and has been wildly applied in commercial and forensic areas. Fingerprint classification, as the fundamental procedure in fingerprint recognition, can sharply decrease the quantity for fingerprint matching and improve the efficiency of fingerprint recognition. Most fingerprint classification algorithms are based on the number and position of singular points. Because the singular points detecting method only considers the local information commonly, the classification algorithms are sensitive to noise. In this paper, we propose a novel fingerprint classification algorithm combining the local and global information of fingerprint. Firstly we use local information to detect singular points and measure their quality considering orientation structure and image texture in adjacent areas. Furthermore the global orientation model is adopted to measure the reliability of singular points group. Finally the local quality and global reliability is weighted to classify fingerprint. Experiments demonstrate the accuracy and effectivity of our algorithm especially for the poor quality fingerprint images.

Paper Details

Date Published: 23 November 2011
PDF: 7 pages
Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80061P (23 November 2011); doi: 10.1117/12.901635
Show Author Affiliations
Chongjin Liu, Peking Univ. (China)
Xiang Fu, Peking Univ. (China)
Junjie Bian, Peking Univ. (China)
Jufu Feng, Peking Univ. (China)


Published in SPIE Proceedings Vol. 8006:
MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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