A Novel Rule-based Fingerprint Classification Approach

Abstract

Fingerprint classification is an important phase in increasing the speed of a fingerprint verification system and narrow down the search of fingerprint database. Fingerprint verification is still a challenging problem due to the difficulty of poor quality images and the need for faster response. The classification gets even harder when just one core has been detected in the input image. This paper has proposed a new classification approach which includes the images with one core. The algorithm extracts singular points (core and deltas) from the input image and performs classification based on the number, locations and surrounded area of the detected singular points. The classifier is rule-based, where the rules are generated independent of a given data set. Moreover, shortcomings of a related paper has been reported in detail. The experimental results and comparisons on FVC2002 database have shown the effectiveness and efficiency of the proposed method.