# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 2 Related Work  
Digital vein recognition technology was first invented by [PERSON] [15] in 1983 and named Veincheck, which is the prototype of modern vein recognition technology. Japanese researchers [PERSON] et al. [16] first proposed the use of finger vein features for identity authentication in 2000. Like other biometrics, finger vein recognition processes mainly include four stages: image acquisition, image pre-processing, feature extraction, and feature matching. So far, many researchers have proposed many interesting methods of finger vein feature extraction.  
Generally speaking, the recognition methods of finger veins are divided into the following two categories: The first type of finger vein recognition method is based on the traditional manual feature extraction. [PERSON] et al. [17] proposed a classical linear tracking algorithm to extract vein pattern features based on a local grayscale difference of finger vein images. [PERSON] et al. [18] presented a method of finger vein pattern extraction based on region growth, and the extraction effect was better. To further improve the effectiveness of feature extraction, [PERSON] et al. [19] also proposed the maximum curvature algorithm for finger vein feature extraction by finding the maximum curvature of a local cross-section of the image, which has become a presentative algorithm in the field of finger vein feature extraction. However, low-quality finger vein images may reduce recognition performance, and [PERSON] et al. [20] proposed the use of local multi-scale matching filters to alleviate the noise generated by uneven illumination in digital vein images. At the same time, global features are used to enhance finger vein images to obtain better recognition performance. [PERSON] et al. [21] proposed a local linear binary pattern feature based on the local feature of finger veins to extract the coding feature of the linear local region and obtained good experimental results. [PERSON] et al. [22] presented a new extraction method for digital vein features, which focused on local invariant directional feature extraction. The extracted features are further processed by GridPCA to remove redundant data, which can obtain a more accurate recognition effect of finger veins.  
The second type of finger vein recognition method is based on the feature extraction methods of deep learning. The goal of deep learning is to learn features and obtain the feature information of each layer through the network, thus solving the problem of manually extracting feature points. [PERSON] et al. [9] proposed a CNN-based finger vein recognition model and tested its effectiveness on four public finger vein image datasets. [PERSON] et al. [23] proposed a CNN-based finger vein recognition model that is robust to image shading and misalignment. This model was tested on three public finger vein databases and achieved good recognition results. [PERSON] et al. [24] presented a finger vein verification algorithm based on fully CNN and conditional random field and tested the proposed model on three public finger vein datasets, and the experimental results verified its superior performance in the finger vein verification task. [PERSON] et al. [25] proposed a Multi-Receptive Field Bilinear Convolutional Neural Network (MRFBCNN) to obtain second-order features of finger veins and better distinguish finger veins, with little difference between classifications.