A Novel Algorithm of Singular Points Detection for Fingerprint Images

Abstract

It is very important to effectively detect singularities (core and delta)for fingerprint matching, fingerprint classification and orientation flow modeling. In this paper, based on multilevel partitions in a fingerprint image, we present a new method of singularity detection to improve the accuracy and reliability of the singularities. Firstly, based on the information of the orientation field, with the Poincaré index method, we detect singularities which are estimated by different block sizes and various methods of orientation field estimation (smoothing or no smoothing). Secondly, based on the corresponding relationship between the singularities detected by multilevel block sizes and by different methods of orientation field estimation, we extract the singularities precisely and reliably. Finally, an experiment is done in the NJU-2000 fingerprint database that has 2500 fingerprints. The result shows that the method performs well and it is robust to poor quality images.