An efficient approach to extract singular points for fingerprint recognition

Fingerprint analysis is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. A core-delta relation is used as a global constraint for the final selection of singular points. This paper presents and discusses rules for implementing a structural fingerprint classifier. The proposed approach is divided in two main phases: Singular points extraction and recognition algorithm. The fingerprint classification is heavily depended on the singularities, i.e., core point and delta points. For the recognition of fingerprint a new technique is proposed based on the number of singular points and their types.