Finger Vein Verification using Intrinsic and Extrinsic Features

Finger vein has attracted substantial attention due to its good security. However, the variability of the finger vein data will be caused by the illumination, environment temperature, acquisition equipment, and so on, which is a great challenge for finger vein recognition. To address this problem, we propose a novel method to design an endto-end deep Convolutional Neural Network (CNN) for robust finger vein recognition. The approach mainly includes an Intrinsic Feature Learning (IFL) module using an auto-encoder network and an Extrinsic Feature Learning (EFL) module based on a Siamese network. The IFL module is designed to estimate the expectation of intra-class finger vein images with various offsets and rotation, while the EFL module is constructed to learn the inter-class feature representation. Then, robust verification is finally achieved by considering the distances of both intrinsic and extrinsic features. We conduct experiments on two public datasets (i.e. SDUMLA-HMT and MMCBNU_6000) and an in-house dataset (MultiView-FV) with more deformation finger vein images, and the equal error rate (EER) is 0.47%, 0.1%, and 1.69% respectively. The comparison against baseline and existing algorithms shows the effectiveness of our proposed method.