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 augmented 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 transformer 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