# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 3 Materials and Methods
### FasterNet Block  
In finger vein recognition projects, traditional network modules typically incur high computational overhead and exhibit a low efficiency in channel and spatial information interaction. Although they may achieve an excellent recognition accuracy, they often result in a slow recognition speed, making it difficult to meet the real-time requirements of practical applications. Utilizing the lightweight FasterNet Block [37] for feature extraction enables efficient feature extraction while maintaining recognition accuracy. As shown in Figure 2a, the FasterNet Block consists of a Partial Convolution (PConv) layer, two \(1\times 1\) convolution layers with a stride of 1, a Batch Normalization (BN) layer, and a GELU activation function. The BN layer normalizes the input features, enhancing the stability and speed of model training, while the GELU activation function improves non-linear expressiveness by smoothly activating the input, thereby enhancing model performance. To further optimize computational efficiency, the FasterNet Block employs a targeted strategy by placing the BN layer and GELU activation function only after the first \(1\times 1\) convolution layer in each module. This reduces redundant computation steps and improves inference speed. Each FasterNet Block maintains the same number of channels, with channel changes occurring only between stages. This design reduces computational complexity and memory consumption while ensuring consistency in feature transmission across modules, allowing the network to better capture both global and local feature relationships and effectively extract structural information from images. Since Partial Convolution operates on only a subset of channels, a Skip Connection is used at the end of the module to fully utilize all of the channel information and prevent feature loss. The input to the Partial Convolution layer is directly passed to the final addition operation, preserving input features and fusing them with the convolution-processed features. This design enhances the model's learning capability and stability, facilitating better retention and transmission of information in deep networks. As a core component of FasterNet Block, PConv is specifically designed to minimize computational redundancy while optimizing memory access. As shown in Figure 2b, the core idea of PConv is to perform regular convolution on a subset of the input feature map channels for spatial feature extraction. To optimize sequential or regular memory access, the first or last consecutive \(c_{p}\) channels are computed, treating them as representatives of the entire feature map. For the number of standard convolution channels \(c\), when the ratio  
Figure 2: (**a**) FasterNet Block structure; (**b**) Partial Convolution (PConv). \(r=\frac{c_{p}}{c}=\frac{1}{4}\), PConv has only \(\frac{1}{16}\) floating-point operations (FLOPs) of a standard convolution. PConv's FLOPs are as follows:  
\[h\times w\times k^{2}\times c_{p}^{2} \tag{1}\]  
In addition, PConv's memory accesses are significantly reduced for  
\[h\times w\times 2c_{p}+k^{2}\times c_{p}^{2}\approx h\times w\times 2c_{p} \tag{2}\]  
This is only \(1/4\) of a standard convolution, and the actual computational efficiency on the GPU is \(10.5\) times higher than that of Depthwise Separable Convolution (DWConv) [37]. This localized computation is highly efficient for processing sparse and irregular vascular textures in finger vein images, substantially reducing the computational resources needed for each feature map. The first \(1\times 1\) Conv after PConv provides higher channel capacity for feature maps, thus enabling the model to flexibly express and integrate features. Since it does not affect the spatial dimensions (acting only on the channels), it can be efficiently transformed and expanded in channel dimensions for the feature vectors at each location, so that the next step of the nonlinear activation layer can perform better.