A Multi-illumination Dataset and an Illumination Domain Adaptation Network for Finger Vein Identification

Near-infrared transmission through the finger can capture the vein structure for identity recognition. However, in outdoor applications, finger vein imaging is significantly affected by environmental illumination resulting in low recognition performance. Existing methods typically address this issue by constructing multi-illumination models, but collecting multi-illumination images from individual is challenging, and overexposure can cause venous structure distortion. This paper proposes MDA-Net, a Multi-illumination Domain Adaptive Network for finger vein recognition, which is engineered to excel in the dynamic outdoor lighting landscape with various conditions including overexposure, using only data collected under a single illumination for training. Firstly, an Illumination Feature Separation Network(IFSNet) is used to remove the illumination components and obtain illumination-invariant features; Then an Absorption Difference Feature Extraction network(ADFENet) is used to reduce the impact of venous structure distortion under illumination conditions, especially overexposure. To replicate the entire range from low-light to overexposure in outdoor scenarios, a novel Multi-Illumination Finger Vein Dataset (MIFVD) is constructed with significant illumination variations. Experimental results show that MDA-Net significantly improves recognition performance under complex illumination conditions, achieving a state-of-the-art (SOTA) average recognition rate of 91.67% and an average equal error rate (EER) of 0.96%. Further validation on public datasets SDU and USM, demonstrates SOTA EERs of 0.16% and 0.10%, respectively. The License for MIFVD can be accessed at: https://github.com/AHU-MedImagingIJR/MIFVD.