Fingerprint classification based on continuous orientation field and singular points

Abstract

Fingerprint classification is crucial to reduce the processing time in a large-scale database. In this paper a fingerprint classification based on continuous orientation field and singular points is proposed. The continuous orientation field can not only filter the noises in point directional image, but also represent the basic structural feature of fingerprint more precisely. Singularities are the most important and reliable feature in classification. The reliable and fast classification algorithm is made possible by a simple but effective combination of continuous orientation field and the modified Poincare index in the determination of singular points.The experiment results show the effectiveness of the proposed method in producing good classification result.