A fingerprint classification algorithm based on combination of local and global information

Chongjin Liu; Xiang Fu; Junjie Bian; Jufu Feng

doi:10.1117/12.901635

23 November 2011 A fingerprint classification algorithm based on combination of local and global information

Chongjin Liu, Xiang Fu, Junjie Bian, Jufu Feng

Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80061P (2011) https://doi.org/10.1117/12.901635
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Chongjin Liu, Xiang Fu, Junjie Bian, and Jufu Feng "A fingerprint classification algorithm based on combination of local and global information", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80061P (23 November 2011); https://doi.org/10.1117/12.901635

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KEYWORDS

Image quality

Reliability

Quality measurement

Detection and tracking algorithms

Fingerprint recognition

Convolution

Forensic science

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