FVT: Finger Vein Transformer for Authentication

In recent years, deep learning-based finger vein (FV) authentication has attracted the attention of biometric researchers and achieved breakthrough results. Previously, convolutional neural networks (CNNs) were the most commonly used deep learning-based methods for FV authentication. Recently, the vision Transformer (ViT)-based method has started getting attention from the research community due to its excellent performance in many computer vision tasks. In this article, we delve into ViTs and propose a novel model, FV Transformer (FVT), for FV authentication. The FVT consists of four key modules: 1) the conditional position embedding, which is capable of dynamically generating position codes according to the input FV tokens; 2) the weight-shared expanded multilayer perceptron (EMLP), which helps to extract richer and more robust token information; 3) the local information-enhanced feedforward network (FFN), which enhances the ability of local information extraction; and 4) the expansion-less mechanism (ELM) for aggregating adjacent FV tokens, which implements the pyramid structure, and hence, the multilevel feature extraction capability is introduced to the Transformer architecture, which originally focuses on global information. To fully validate the performance and generalization of FVT, experiments were conducted on nine publicly available FV datasets. The effectiveness of each key module of FVT is demonstrated in the ablation experiments. Also, the comparative experiments show that the FVT outperforms several baseline Transformer models and achieves competitive performance when compared with the state-of-the-art (SOTA) FV authentication methods.