Study on Reflection-Based Imaging Finger Vein Recognition

Abstract

Finger vein modality plays an important role in biometrics due to its stability and security. However, existing state-of-the-art finger vein recognition systems adopt the transmission-based imaging mode with a sealed design, which requires redundant space in the imaging device and results in an uncomfortable user experience. Consequently, we design a reflection-based imaging device with an open structure to reduce the device volume and improve the portability, as well as the user experience. However, an open structure of the device inevitably introduces extra illumination variation to the image, which may deteriorate the performance of the system. In this paper, we propose Domain Adaptation Finger Vein Network (DAFVN) to narrow the domain shift between different illumination data domains and extract illumination-invariant features from finger vein images, improving the robustness to illumination variations. To evaluate the performance of DAFVN and remedy the lack of a publicly open reflection-based finger vein database, we use the self-made device to construct the first large-scale reflection-based finger vein database, namely SCUT Reflective Imaging Finger Vein database (SCUT-RIFV). It includes 32,064 images from 167 subjects with five different illumination conditions. Abundant experiments implemented on the SCUT-RIFV database indicate that the proposed method can effectively alleviate the influence of illumination variation on the reflection-based finger vein recognition system.