Finger Vein Recognition Based on Fusion of Deformation Information

As a convenient and fraud-proof biometric, finger vein has received increasing attention from researchers. However, the deformation problem in finger vein images is still a challenge preventing the performance of finger vein recognition from prefect. In our opinion, the deformations are distinguishable between genuine and imposter fingers rather than performing as disturbing noises. It is because that the diversity between genuine images is mainly caused by different finger postures, and this difference can be characterized and utilized. While in the imposter matching, it is not the case. In this paper, we design a framework to ensemble the encoded deformation information with traditional texture-based method to improve the recognition performance. First, the pixel-level features are extracted to represent the finger vein images. Next, the best matching for each pixel is discovered by an optimized matching. Consequently, the displacement of each pixel is obtained, and the texture features of deformation is extracted from the displacement matrices. Furthermore, the results of direct and optimized matching for pixel-level features, together with the encoded deformations are fused using weights learned by the Support Vector Machine (SVM) model. Evaluated on the publicly available databases HKPU and SDU-MLA, extensive experiments demonstrate the superiority of the proposed method, with the Equal Error Rates (EERs) of 0.0040 and 0.0266 respectively on the two databases, which are much lower than recognition solely with deformation information. The proposed method is also applicable in high security scenarios, the False Reject Rate at zero False Accept Rate (FRR-at-0-FAR) can be reduced to 0.0363 on the HKPU database through adjusting the fusion parameters.