Research On Key Problem Of Finger Vein Recognition

In modern society, with the rapid development of information technology and the continuous extend of human physical and virtual space, human identity recognition is be put forward higher requirements for safety and practicality. One of the most major characteristics of rapid development of information technology is the digital and hidden of the personal identity. Thus, how to identify a person’s identity accurately and protect the information security is a key social problem in today’s information age.Traditional identification methods can’t meet the requirements above. Human beings must seek a new way of identification with high security and convenience. Thus, the identity recognition based on biometrics has attracted more and more researchers’ attention. Biometrics refers to verify identity using some physiological or behavioral characteristics of human body itself. Compared to traditional biometrics such as fingerprint recognition, finger vein recognition is a new biometric technology which has some better prospects.Currently, most of the finger-vein recognition methods are based on pixel-level features. These low-level features are mainly extracted on the whole image or the finger-vein networks. And they have some problems. The finger-vein recognition methods based on the whole image will get a lot of redundant data and their time complexity is high. The finger-vein recognition methods based on the finger-vein network will be sensitive to the image segmentation. Thus in order to improve the effectiveness of the features so as to improve the recognition performance, in this paper, we propose a new finger vein recognition method based on singular value decomposition (SVD). In addition, in order to eliminate the impact of the noise onthe single pixel, we used superpixel feature information and propose a novel method.This paper firstly gives the overviews of the key algorithms in our work, including the superpixel over-segmentation, the singular value decomposition, and the spatial pyramid matching kernel, besides, introduces their research background, algorithm flow and application; secondly, respectively from the research motivation, process, and the experimental results, it introduces two new methods. One is the SVD-based finger vein recognition and the other is superpixel-based finger-vein recognition; finally, we summarize the work in this paper and prospect the follow-up work.The SVD-based finger vein recognition method involves three stages:(Ⅰ) minutia pairing, (Ⅱ) false removing and (Ⅲ) score calculating. In particular, stage Idiscovers minutia pairs via SVD-based decomposition of the correlation-weighted proximity matrix. Stage Ⅱ removes false pairs based on the local extensive binary pattern (LEBP) for increasing the reliability of thecorrespondences. Stage Ⅲ determines the matching score of the input and template images by the’average’.The superpixel-based finger vein recognition method utilizes superpixel-based features of finger vein for high level feature representation. When comparing two finger veins, the features of each pixel are firstly extracted as base attributes. Then, after superpixel over-segmentation, the SPF of eachfinger vein can be obtained based on its base attributes by some statistical techniques. Lastly, a weighted spatial pyramid matching scheme is utilized to implement matching.Extensive experiments demonstrate that the methods notonly perform better than the similar works in the literature, but also have great potential to achievecomparable performance to other categories of state-of-the-art methods.For future extensions of our work, we are interested inimproving the accuracy of the minutiae extraction algorithm usedin this paper and utilizing some weighted fusion methods forfeature combination.