The hand vein pattern as a biometric trait for identification has attracted increasing interests in recent years thanks to its properties of uniqueness, permanence, non-invasiveness as well as strong immunity against forgery. In this paper, we propose a novel approach for back of the hand vein recognition. It first makes use of Oriented Gradient Maps (OGMs) to represent the Near-Infrared (NIR) hand vein images, simultaneously highlighting the distinctiveness of vein patterns and texture of their surrounding corium, in contrast to the state-of-the-art studies that only focused on the segmented vein region. SIFT based local matching is then performed to associate the keypoints between corresponding OGM pairs of the same subject. The proposed approach was benchmarked on the NCUT database consisting of 2040 NIR hand vein images from 102 subjects. The experimental results clearly demonstrate the effectiveness of our approach.
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