High Security Finger Vein Recognition Based on Robust Keypoint Correspondence Clustering

Finger vein recognition has been proven to be an effective pattern for personal versification in terms of its convenience and security. However, the existing works of finger vein recognition have neglected the application scenarios of finger vein recognition and treated the false acceptance rate (FAR) and the false rejection rate (FRR) equally, i.e., utilized the equal error rate (EER) as the main evaluation criterion. As structures hidden beneath the skin, the finger vein pattern is usually applied in access controls rather than forensics. Hence, the security requirement of finger vein recognition should be high, i.e., the FRR is assumed to be reduced under the premise of extremely low FAR. In our opinion, the important points and difficulties related to achieving high security recognition are enlarging the differences between genuine and imposter matchings. In this paper, a finger vein recognition framework based on robust keypoint correspondence clustering is proposed to achieve high security recognition. A scale-invariant feature transform (SIFT) descriptor-based method is utilized as the base recognizer. Then, a multi-input multi-output (MIMO) matching structure is designed according to different physical characteristics of the finger vein images to enhance the matching possibilities. After that, integrations of the matching pairs of each correspondence (i.e., matching of two images) are clustered according to the deformation information of each matching pair by a novel simulated clustering technique. Finally, the matching score is defined as the number of matching pairs after clustering. Extensive experiments on HKPU and FV-SDUMLA-HMT open databases demonstrate the superior performance of the proposed method, with the FRRs-at-0-FAR of 0.0139 and 0.2377, respectively, which imply the applicability of the proposed method in high security scenarios. The corresponding EERs are 0.0015 and 0.0139, and the rank-one recognition rates are 99.91% and 97.54%, respectively, which are comparable to the state-of-the-art methods and further indicate the effectiveness of the proposed method.