A Lightweight Vision Transformer with Competitive Blocks for Finger Vein Recognition

With the widespread adoption of online payment systems, concerns over the security of financial transactions have grown significantly. Finger vein recognition has emerged as a promising biometric solution, offering enhanced security and reliability. However, existing deep learning-based finger vein recognition methods often suffer from high computational complexity and limited feature representation capabilities. To address these limitations, this study proposes a novel Lightweight Vision Transformer with Competitive Blocks (LViT-CB) architecture for finger vein recognition. The LViTCB model enhances feature representation through a competitive mechanism, thereby improving the Correct Identification Rate (CIR). Experimental results demonstrate that the proposed model achieves CIRs of 9 9. 9 3%, 99.17%, and 98.92% on the FV-USM, PLUSVein-FV3 (LED), and PLUSVein-FV3 (Laser) public datasets, respectively. In terms of Equal Error Rate (EER), the model attains 0. 0 6%, 1. 1 9%, and 1.09% on the corresponding datasets. Moreover, the LViTCB model contains only 1.06 million parameters and operates with a computational cost of just 0.27 GFLOPs, making it highly suitable for deployment on resource-constrained platforms, such as those used in embedded systems for financial applications.