Fingerprint Singular Point Detection via Quantization and Fingerprint Classification

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 2x2 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.