Biometrics is widely used in life, and finger-vein recognition also has preliminary applications. Finger-vein recognition methods based on deep learning have achieved state-of-the-art performance, but there are still some challenges, such as sensitivity to finger posture, low recognition efficiency, etc. In our study, a data augmentation strategy using the random sliding window is designed to alleviate the problem caused by changes in finger posture. In addition, to improve the recognition efficiency while ensuring the accuracy, a lightweight finger-vein feature extraction model is proposed based on vision transformer, in which the tokens to token structure is able to describe the global correlation of images at multiple scales. Experimental results on three public databases indicate that compared with other deep-learning methods, the proposed method has achieved higher recognition efficiency and accuracy.