The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.
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