Hand vein recognition system with circular difference and statistical directional patterns based on an artificial neural network

In this article, a novel hand vein pattern recognition process for human identification is presented. Hand vein characteristics can be considered as more reliable in biometric domain compared with other biometric characteristics, such as palmprint and fingerprint, because veins are located in volume, making features more robust to test conditions. In this paper, a rotation invariant texture descriptor called Circular Difference and Statistical Directional Patterns (CDSDP) is proposed to extract hand vein patterns. Its histogram is considered as attribute vector. The CDSDP is a surrounding circular difference with weights incorporating the statistical directional information of vessels. Experimental results show that the proposed descriptor based on CDSDP has better performance than the previous descriptors used in local binary patterns (LBP). The proposed method gives an Identification Rate (IR) of 99.8 % and an Error Equal Rate (EER) of 0.01 %. Furthermore, the average processing time of the proposed method is 5.2ms for one hand vein posture, which satisfies the criterion of a real time hand vein recognition system.