Abstract
Finger-vein recognition technology has attracted more and more attention because of its high security and convenience. However, the finger-vein image capturing is affected by various factors, which results that some vein patterns are missed in acquired image. Matching minutiae features in such images ultimately degrades verification performance of the finger-vein recognition system. To overcome this problem, in this paper, a novel finger-vein image restoration approach is proposed to recover the missed patterns based on generative adversarial network (GAN), as the first attempt in this area. Firstly, we employ the segmentation algorithm to extract finger-vein network, which is further subject to thinning operation. Secondly, the resulting thinning image is taken as an input of a GAN model to restore the missed vein patterns. Thirdly, the minutiae points are extracted from restoration finger-vein pattern. Finally, we propose a matching approach for verification. Experimental results show that the proposed method can restore the missed vein pattern and reduce the equal error rate (EER) of the finger-vein verification system.
Highlights
With the rapid development and application of the Internet, people pay more and more attention to the security protection of individual identity
We develop, in this paper, a novel finger-vein restoration scheme based on generative adversarial network (GAN) to restore finger-vein patterns
We propose a generative adversarial network to recover the vein patterns for verification
Summary
With the rapid development and application of the Internet, people pay more and more attention to the security protection of individual identity. Matching such minutiae features will degrade the performance of vein verification system To overcome this problem, a lot of approaches have been proposed to restore the finger-vein pattern, showing good performance on different databases. Many incorrect minutiae features are generated and matching them may degrade the verification accuracy To overcome this problem, first, we extract the vein network using image segmentation approach and the resulting vein patterns are further thinned to obtain skeleton image. These corrupted images show poor connectivity because some vein patterns are missed, which results many incorrect minutiae patterns such as endpoint and bifurcation point. The minutiae patterns are extracted and matched for verification
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