Hybrid convolutional transformer-based network model for finger vein identification

In recent years, finger vein (FV) recognition has garnered significant attention due to
its inherent advantages, such as enhanced security, convenience, and the ability to
discern living organisms. Notably, use of vision transformers in FV recognition has
yielded promising results, primarily owing to their adeptness in capturing extensive
spatial relationships within images. Nevertheless, transformers necessitate aug-
mented computational resources and presently fall short compared to established
convolutional neural networks (CNNs) concerning performance metrics. We address
this predicament and propose the development of an advanced network that
surpasses conventional transformers and convolutional networks. By leveraging
transformers to capture long-range dependencies and CNNs to extract localized
information, we introduce a hybrid architecture named the FV convolutional trans-
former for FV identification. We validate the efficacy of our approach by conducting
extensive experiments on three publicly available FV databases. The experimental
results demonstrate that our network achieves state-of-the-art performance, as
evidenced by the attainment of the lowest equal error rate across all three datasets