# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 1 Introduction
Moreover, the pooling layer of CNN loses a lot of valuable information, which makes the result of finger vein recognition unable to achieve a great improvement. [PERSON] et al. [12] presented the capsule network, which defined features in a more reasonable way than CNN by ensuring the invariance of translation and rotation. [PERSON] et al. [13] used capsule networks for recognition using a limited number of samples in four finger vein datasets. Although the capsule network overcomes some of the drawbacks of CNNs, the model cannot selectively focus on the important information in the image, resulting in a much smaller receptive field during actual processing than the theoretical receptive field. In fact, after we detected the key points, object boundaries, and other basic units that made up visual elements, high-level visual semantic information tended to focus more on how these elements related to each other to form a target object and how the spatial position relationships between these target objects constituted a scene. However, models such as the capsule network do not achieve the desired effect when dealing with the relationships between these elements. [PERSON] [14] put forward a transformer model applied to computer vision, which achieved good results on multiple image recognition benchmarks. Unlike CNN models, vision transformer uses a self-attention mechanism to integrate information across the entire image. Even at the lowest level, the vision transformer captures global contextual information by using self-attention to establish remote dependencies on targets and extract more powerful features. To solve the problem that the capsule network lacks the ability to encode the long-range dependencies in the image and cannot selectively pay attention to important image feature information when the capsule network is used for image classification, we integrated the advantages of the capsule network in processing the underlying vision information, as well as the advantages of the transformer in processing the relationship between the visual elements and the target objects, and propose a new vision transformer-based capsule network model (ViT-Cap) for finger vein recognition. The model can encode the dependencies between image features so as to improve the effect of image classification, especially multi-label classification. Experimental results showed that the proposed model has better recognition performance than the existing recognition methods. The main contributions of this article are as follows. A new vision transformer model based on a capsule network is proposed for finger vein recognition. 1. The model can encode long-range dependencies in images and can better understand the relationship between advanced semantic visual information and basic visual elements in finger vein images. 2. Based on the vision transformer, we introduced the local information shared by the capsule network module and constructed a finger vein recognition model based on global attention and local attention, which better obtained the features of finger veins and maximized the performance of the model. 3. Compared with CNNs, the model we proposed has higher logical interpretability, and we visualized part of the training process of the model. At the same time, due to the dynamic routing mechanism in the model, better performance was obtained when processing small-scale image data, which solved the limitation of a small amount of finger vein data. The remainder of this article is organized as follows: Section 2 briefly introduces the relevant work of finger vein recognition methods; Section 3 describes the motivation behind our proposed new model and the main methods based on our proposed model; Section 4 gives the detailed experimental results and analysis; and finally, we summarize the work and look forward to the future work in Section 5.