Deep Patch Matching For Hand Vein Recognition

Hand vein recognition for personal identification has attracted considerable attention from scholars due to the uniqueness of vein pattern and nearly impossible forgery. However, recognition can be difficult to obtain when remarkable discrepancies exist in vein images caused by inconstant hand poses during imaging. This study proposes deep patch matching methodology (DPM) for hand vein recognition to solve significant differences in vein images. The proposed method calculates the similarity map of each circle patch based on vessel-enhanced filters using convolution. All similarity maps are further aggregated to calculate image similarity using max-pooling. The proposed algorithm is evaluated using public NCUT part A database. The recognition rate of the proposed algorithm is 99.71%. Experimental results reveal that the proposed algorithm obtains the highest accuracy among existing methods.