# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 2 Related Works
[29] proposed a finger vein verification method based on Convolutional Autoencoder (CAE) combined with Support Vector Machines (SVMs), where high-level feature representations of finger vein images are extracted by using CAEs, and these features are classified by SVMs. [PERSON] et al. [30] proposed a lightweight convolutional neural network model based on the pre-trained network structure of MobileNetV2 and by incorporating customized auxiliary network modules designed to optimize the training process and improve the finger vein recognition performance. [PERSON] et al. [31] proposed a template-protected finger vein authentication system based on template protection and efficient authentication through the application of RSA encryption and a convolutional neural network. FV-ViT [32] incorporated Vision Transformer (ViT) into the finger vein recognition task, enhanced its architecture with regularized MLP (regMLP), and optimized the model performance by training it from scratch. SegNeXt [33] shows that, compared to the self-attention mechanism in Transformers, convolutional attention is more effective and efficient. [PERSON] et al. [34] proposed a multi-scale convolutional network called MC-Net, which enhances structural features through the Multi-Scale Feature Extraction (MFE) module and improves local feature representation using the Cross-Information Fusion Attention (CFA) module. This approach addresses the limitations of deep learning methods in finger vein recognition, particularly in capturing long-texture features and local details. [PERSON] et al. [35] proposed an automatic finger vein recognition method based on the Swin Transformer (SwinT) and the Super Graph Gluing (SGG) model to reduce dependence on domain knowledge and optimize finger vein feature extraction through deep learning. This approach enhances the accuracy of feature extraction and matching by integrating multi-stage preprocessing, advanced feature extraction, and an optimized classification framework. [PERSON] et al. [36] proposed a feature extraction algorithm based on an open-set testing protocol, which enhances vein feature learning through segmentation-assisted classification. The method integrates multi-scale feature fusion and spatial attention while employing a hybrid loss function to improve feature discriminability, achieving promising recognition performance on public datasets. This paper proposes FMFVNet, a finger vein recognition network that balances both recognition speed and accuracy. The model utilizes the FasterNet Block as its primary feature extraction module. By utilizing its unique partial convolution, FMFVNet reduces computational redundancy and memory access, decreases model complexity, and enhances actual computational efficiency, thereby shortening inference time. To further improve recognition accuracy and mitigate the impact of factors such as finger position variations, uneven illumination, and noise from image acquisition devices, this paper introduces Multi-Scale Convolutional Attention (MSCA) to enhance the model's multi-scale feature extraction capability, allowing it to focus more on vein features while ignoring irrelevant background information. The main contributions of this work can be summarized as follows:  
1. This study proposes an efficient finger vein recognition network called FMFVNet to meet the requirements of finger vein recognition tasks for both accuracy and efficiency. Experimental results demonstrate that the proposed model achieves a higher recognition accuracy and lower inference time compared to other methods while maintaining fewer parameters and a lower computational complexity. 2. In this study, MSCA is introduced for finger vein recognition. By incorporating multi-branch deep strip convolutions of different scales, the model captures rich details of finger vein images at multiple scales, focusing more on vein feature extraction. Experimental results show that integrating MSCA effectively improves the recognition accuracy of the finger vein recognition model. 3.