Palmprint recognition using crease

Abstract

The palmprint has one special salient feature which is not salient in fingerprint. That is the crease. In a palmprint the creases are large in number and comparatively easy to extract. Creases are also approximately stable in a person's whole life, which qualifies them as features in palmprint recognition. We give creases an accurate definition which is fit for algorithm implementation. We devised a rather exquisite algorithm to extract all the creases in a palmprint whose success is mainly from a new different direction computing method and a thorough local analysis and a robust search algorithm. Based on the extracted creases, we devised a robust palmprint matching algorithm which is rotation and translation invariant. The crease extraction results and palmprint matching results show that the crease can be extracted successfully and crease-based palmprint matching is robust and accurate.