Fingerprint classification based on extraction and analysis of singularities and pseudoridges

Authors:
Qinzhi Zhang;Kai Huang;Hong Yan
Affiliations:
School of Electrical and Information Engineering, University of Sydney, NSW 2006, Australia;School of Electrical and Information Engineering, University of Sydney, NSW 2006, Australia;School of Electrical and Information Engineering, University of Sydney, NSW 2006, Australia
Venue:
VIP '01 Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11
Year:
2001

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Abstract

In this paper, we introduce a new approach to fingerprint classification based on both singularities and traced pseudoridge analysis. Since noise exists in most of the fingerprint images including those in the NIST databases which are used by many researchers, it is difficult to get the correct number and position of the singulairities such as core or delta points which are widely used in current structural classification methods. The problem is we may miss the true singular points and/or get false singular points due to the poor quality of fingerprint images. Classification based on exact pair of singulairities will fail in such conditions. With the help of the pseudoridge tracing and analysis of the traced curve, our method does not rely on the extraction of the exact number and positions of the true singular points, thus improving the classification accuracy. This method has been tested on the NIST-4 fingerprint database. For the 4000 images in this database, the classification accuracy is 95.3% with 11.8% reject rate for 4-class problem (combining Arch and Tented Arch as one class).