Volume 5, Issue 12, 2015

By | August 11, 2018

Fingerprint Singular Point Detection via Quantization and Fingerprint Classification

Shing Chyi Chua, Eng Kiong Wong, Alan Wee Chiat Tan
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.

Abstract—This paper aims to present a fingerprint singular point detection algorithm and a rule-based fingerprint classification method. The singular point detection algorithm uses a quantization approach on the orientation field of the fingerprint image and seeks to locate the core and delta points via the changes of the gray levels around a 2×2 window. It has been found that with the application of an edge-trace-cum-core-delta-pairing algorithm and a merging-and-pruning heuristic as the post-processing steps, spurious singular points are removed and the final singular points are then used for classification. Fingerprint classification on NIST-4 database by rule-based method utilizes the number of singular points and three key geometry features to perform 5-class as well as 4-class classification using success rate (the accuracy) as the performance measure. It has been found to achieve 86.5% and 92.15% of success rate, respectively. The study has thus find the application of the new singular point detection algorithm via quantization and the rule-based classification to be promising as many of the fingerprint images in the NIST-4 database have been reported as poor quality, i.e. 22.35%.

Keywords-singular point; fingerprint singular point detection; fingerprint classification; quantization; fingerprint orientation field.

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