# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 3 Materials and Methods
### Multi-Scale Convolutional Attention (MSCA)  
In finger vein recognition tasks, since finger vein images are captured under near-infrared light, the contrast between the veins and the background may sometimes be relatively low. Additionally, factors such as uneven finger illumination can introduce significant background interference and noise. Figure 3 illustrates a clear finger vein image alongside images with uneven illumination and low contrast. Traditional networks often rely on single-scale convolution for feature extraction, making it difficult to simultaneously capture both local details and global information. As a result, their representation of finger vein texture details is insufficient, leading to poor robustness. The Multi-Scale Convolutional Attention (MSCA) module [33] combines the advantages of multi-scale convolution and attention mechanisms, effectively addressing the aforementioned challenges in finger vein recognition. As shown in Figure 4, the input feature map first passes through a BN layer to stabilize the training process and accelerate model convergence. It then goes through a \(1\times 1\) Conv layer, where the GELU activation function introduces non-linearity to enhance the model's expressiveness. Next, the feature map enters the Multi-Scale Convolutional Attention (MSCA) module for further processing and optimization. After being processed by MSCA, the feature map passes through another \(1\times 1\) convolution layer and is then added to the original input feature map via a skip connection. This fusion of the original features with the attention-adjusted features helps retain the original information while further enhancing the representation of key features. The Multi-Scale Convolutional Attention (MSCA) module consists of three main components. First, a \(5\times 5\) Depthwise Separable Conv (DWConv) is applied to the input feature map for initial spatial feature extraction, aggregating local information. Next, the feature map undergoes multi-branch depth-wise strip convolutions at different scales to capture multi-scale contextual information. These features are then fused through skip connections, forming a richer feature representation. Subsequently, the fused features pass through a \(1\times 1\) convolution layer to model the relationships between different channels. The output of the \(1\times 1\) convolution serves directly as the attention weights for reweighting. Finally, the reweighted feature map is element-wise multiplied with the original input feature map via a residual connection, ensuring selective and dependent feature fusion. This approach retains original information while further enhancing the representation of key features. By using different types of residual connections, the module maintains feature propagation while increasing flexibility. Through the combination  
Figure 4: Overall structure of the Multi-Scale Convolutional Attention (MSCA) module. Figure 3: (**a**) Clear image; (**b**) uneven illumination image; (**c**) low-contrast image. of multi-scale convolutions and depthwise separable convolutions, the MSCA module demonstrates excellent performance in handling complex image tasks. Specifically, in finger vein recognition, which requires fine-grained details, MSCA significantly improves recognition accuracy while maintaining high computational efficiency. Mathematically, MSCA can be expressed as follows:  
\[Att=Conv_{1\times 1}\left(\sum_{i=0}^{3}Scale_{i}(DW-Conv(F))\right) \tag{5}\]  
\[Out=Att\otimes F \tag{6}\]  
Here, \(F\) represents the input features; Att and Out denote the attention map and the output, respectively; and \(\otimes\) represents the element-wise matrix multiplication operation. DWConv stands for depth-wise separable convolution, while \(\text{Scale}_{i}\) (\(i\in\{0,1,2,3\}\)) represents the \(i\)-th branch in the diagram, where \(\text{Scale}_{0}\) is an identity connection. In each branch, we use two depth-wise strip convolutions to approximate a standard depth-wise convolution with a large kernel.