An automated system for fingerprint classification using singular points for biometric security

Automated fingerprint identification system (AFIS) is very popular now days for biometric security because of the uniqueness of individual's fingerprint. The need for fingerprint classification arises due to very large fingerprint databases resulting in long response time which is unsuitable for real time applications. Hence in order to reduce number of comparisons fingerprint classification is necessary. It also plays a key role in identifying fingerprints. In this paper we have proposed a new classification technique based on the detection of singular points (core and delta points) consisting of four stages. In the first stage, preprocessing of input fingerprint image is done followed by fine orientation field estimation in second stage. In the third stage, singular points are located using modified Poincare index technique and hence in the fourth stage, classification is done on the basis of these singular points. The proposed technique was tested on NIST 4 database and the results show a significant improvement in classification of different types of fingerprints.