Personal authentication based on wrist and palm vein images

One of the newest promising biometrics researched today is the vein pattern recognition. However, little efforts have been invested in this direction. In this paper, two frameworks focused on a palm and wrist vein-based multimodal authentication system are proposed. For the first framework, wrist and palm traits of the same hand are fused, whilst four biometric markers are combined in the second framework using texture descriptors such as local phase quantisation (LPQ), local binary patterns (LBPs), binarised statistical image features (BSIF) and local ternary patterns (LTP). In addition, two approaches of score level fusion are applied: 1) transformation-based using sum rule, min-max rules and t-norms; 2) classifier-based via t-norms. The experimental results on publicly available dataset show that the integration of wrist and palm vein images from both left and right hand gives much improved accuracy than the fusion of two traits of one hand. The recognition rate of the proposed wrist-palm vein based multibiometric system is found to be 100%.