A pre-trained deep convolutional neural network (DCNN) is adopted as a feature extractor to extract the feature representation of vein images for hand-dorsa vein recognition. In specific, a novel selective deep convolutional feature is proposed to obtain more representative and discriminative feature representation. Extensive experiments on the lab-made database obtain the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.

Hand-dorsa vein recognition is solved based on the convolutional activations of the pre-trained deep convolutional neural network (DCNN). In specific, a novel task-specific cross-convolutional-layer pooling is proposed to obtain the more representative and discriminative feature representation. Rigorous experiments on the self-established database achieves the state-of-the-art recognition result, which demonstrates the effectiveness of the proposed model.