# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 3 Materials and Methods  
### Network Architecture  
Figure 1 illustrates the overall network architecture of the proposed FMFVNet. FM-FVNet consists of four feature extraction stages, with spatial downsampling and channel expansion performed through convolutional units between each stage. Before the first stage, a \(4\times 4\) convolution layer is used as the embedding layer, with a stride of 4. This convolution operation maps the input finger vein image into a high-dimensional feature space, generating feature representations rich in semantic information. This serves as the initial step for transforming raw image data into high-dimensional features that the network can process. Before the following three stages, \(2\times 2\) standard convolution layers are used as merging layers, which reduce the spatial dimensions of the feature maps while preserving and integrating important features, thereby improving the efficiency of subsequent feature extraction. In the feature extraction stages, the FasterNet Block is employed as the primary feature extraction module due to its low parameter count and high computational efficiency, which helps improve the model's inference speed while maintaining recognition accuracy. After the four feature extraction stages, a Global Average Pooling Layer, a \(1\times 1\) convolution layer, and a Fully Connected Layer are used for feature transformation and classification. Table 1 presents the detailed architecture of the entire network, specifically illustrating the feature extraction process and dimensional changes in the proposed network. The table lists the spatial resolution, key operations, and final output results at each stage of the network, where the input and output dimensions are represented as width \(\times\) height \(\times\) channels. The model takes as input a \(224\times 224\times 3\) finger vein image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Input Size** & **Output Channels** & **Output Size** & **Operation** \\ \hline Embedding Layer & \(224\times 224\times 3\) & 40 & \(56\times 56\times 40\) & \(4\times 4\) Conv \\ Stage 1 & \(56\times 56\times 40\) & 40 & \(56\times 56\times 40\) & FasterNet Block \(\times 1\) \\ Merging Layer & \(56\times 56\times 40\) & 80 & \(28\times 28\times 80\) & \(2\times 2\) Conv \\ Stage 2 & \(28\times 28\times 80\) & 80 & \(28\times 28\times 80\) & FasterNet Block \(\times 2\) \\ Merging Layer & \(28\times 28\times 80\) & 160 & \(14\times 14\times 160\) & \(2\times 2\) Conv \\ Stage 3 & \(14\times 14\times 160\) & 160 & \(14\times 14\times 160\) & MSCA + FasterNet Block \(\times 8\) \\ Merging Layer & \(14\times 14\times 160\) & 320 & \(7\times 7\times 320\) & \(2\times 2\) Conv \\ Stage 4 & \(7\times 7\times 320\) & 320 & \(7\times 7\times 320\) & FasterNet Block \(\times 2\) \\ Global Pooling Layer & \(7\times 7\times 320\) & 320 & \(1\times 1\times 320\) & Global Average Pooling \\ \(1\times 1\) Conv & \(1\times 1\times 320\) & 1280 & \(1\times 1\times 1280\) & \(1\times 1\) Conv \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spatial resolution, number of channels, and applied operations of each stage in FMFVNet. In the first stage, a single FasterNet Block is used for initial feature extraction, mapping the input finger vein image from the original pixel space to a lower-dimensional feature space. Since this is the initial stage of the model, the extracted features are relatively simple, and the computational demand is low; therefore, a single lightweight network module is sufficient to meet the requirements. After passing through the first Merging layer, the feature complexity increases to a moderate level. A slight increase in the number of lightweight network modules helps enhance the model's feature extraction capability while maintaining a better balance between computational efficiency and recognition accuracy. Therefore, in the second stage, two FasterNet Blocks are used to further refine and enhance the feature representation, improving the model's ability to capture finger vein patterns.