Finger knuckle print recognition for personal authentication based on relaxed local ternary pattern in an effective learning framework

Abstract

Finger knuckle print (FKP) as a physiological trait with a small image dimension, also a highly distinctive pattern, can be used as a reliable biometric identifier. In this paper, a new effective biometric authentication system using FKP texture based on relaxed local ternary pattern (RLTP) is presented. To further improve performance, cascading, overlapped patching and uniform rotation invariant pattern selection are used. Also to obtain more discriminative dominant patterns, an efficient learning framework is integrated with RLTP feature vectors. Identification and verification experiments conducted on the standard PolyU FKP dataset show the effectiveness of the proposed scheme.