Dorsal hand vein biometrics with a novel deep learning approach for person identification

Hand dorsal biometric recognition system proposed in this study combines the strength of information in regions of dorsal vein biometric trait in a deep learning based Convolutional Neural Networks (CNN) model. The approach divides each dorsal image into five overlapping regions; consequently, five different training and test sets are obtained for each image, modeling a multi-modal biometric system while using only one trait. The test outputs are combined by score-level fusion. Experimental results on FYO, Bosphorus and Badawi datasets indicate the efficiency of the proposed method and its comparability with other recognition systems. The results are also compared with the state-of-the-art dorsal hand vein recognition systems to show the ability of the proposed biometric architecture to perform well in different conditions that may affect dorsal vein pattern acquisition and have con-sequent effect on the efficiency of the recognition system.