Palm Vein Recognition Under Unconstrained and Weak-Cooperative Conditions

Contactless palm vein has attracted significant attention for its high security, stability, and user-friendliness. However, current contactless palm vein recognition predominantly relies on databases collected from platforms with spatial and temporal constrained design, which inadequately reflect relaxed palm vein imaging circumstances. This paper proposes a novel manner called on-the-fly palm vein that frees the user’s palm from spatial and temporal constraints, enabling palm vein recognition under unconstrained and weak-cooperative conditions. Firstly, Designing efficient and user-friendly palm vein imaging and authentication via two dynamic palm motions is proposed, resulting in an on-the-fly palm vein recognition platform. Next, a large-scale and challenging palm vein database, SCUT Palm Vein Database Version 1 (SCUT_PV_v1), is constructed. It is the first palm vein database with images collected under unconstrained and weak-cooperative conditions, encompassing a wider range of palm pose variations, grayscale variations, and lower-quality images. Finally, a lightweight and efficient Adaptive Margin Palm Vein Authentication Network (AMPVNet) is proposed as a baseline for the SCUT_PV_v1, where a vein pattern-specific convolutional neural network (CNN) is designed to extract features and a tailored online data augmentation method, combining Random Perspective Transformation (RPT) with Random Grayscale Adjustment (RGA), is proposed to enrich the diversify of out-of-plane palm pose and grayscale variations. Extensive experimental results demonstrate the effectiveness of our proposed methods. As the first work for palm vein recognition under unconstrained and weak-cooperation conditions, the AMPVNet achieves a promising accuracy and computation result while maintaining robustness to palm pose and grayscale variations. The SCUT_PV_ v1 database will be public at
https://github.com/SCUT-BIP-Lab/SCUT_PV_v1
.