# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 4 Experiments and Results
### Comparative Experiments with Different Models  
To more accurately and objectively evaluate the performance of the proposed model in this chapter, we conducted a comprehensive assessment from two perspectives: recognition accuracy and recognition efficiency. First, for a finger vein recognition system, ensuring a high recognition accuracy is crucial. To measure the model's recognition accuracy, we adopted accuracy (ACC) as the evaluation metric, where a higher ACC indicates more precise recognition and enhanced model security. ACC is suitable for classification tasks with balanced class distributions. It is computed by dividing the number of correctly predicted samples by the total number of samples and multiplying by 100% to express the final result. The formula for calculating ACC is given as follows:  
\[ACC=\frac{Number\ of\ Correct\ Predictions}{Total\ Number\ of\ Predictions}\times 100\% \tag{9}\]  
To comprehensively evaluate the accuracy of the proposed FMFVNet model, we compared it with several representative models that have been applied in the field of finger vein recognition, including MobileNetV2 [40], EfficientNet-B0 [41], ResNet-50 [42], and Swin-T [43]. Among these models, MobileNetV2 and EfficientNet-B0 are lightweight convolutional neural network architectures that have gained significant popularity in computer vision in recent years. They are particularly suitable for resource-constrained devices, such as mobile or embedded systems, as both architectures effectively optimize computational efficiency and model size. ResNet-50, with its strong feature extraction capability, is well-suited for processing complex textures and detailed features in finger vein images. It has been widely used as a backbone network for finger vein recognition to extract deep vein features. Swin-T is an attention-based network belonging to the Transformer architecture family. It adopts a shifted window attention mechanism to optimize the traditional Transformer's global self-attention computation, enabling precise modeling of the correlation between local and global features in finger vein images. Additionally, we compared FMFVNet's recognition accuracy with several other models specifically designed for finger vein recognition, including Coding SA [30], LFVRN-CE [31], and FV-ViT [32]. Table 2 presents the accuracy of different models on the FV-USM and SDUMLA-HMT datasets. From the table, we can observe that the deep residual structure of ResNet-50 enables it to capture more complex features, leading to excellent performance and a higher recognition accuracy compared to the lightweight networks MobileNetV2 and EfficientNet-B0. However, Swin-T exhibits lower recognition accuracy than other models. This is because the Transformer architecture relies heavily on large datasets for training, and when the dataset size is relatively small, the sliding window attention mechanism cannot be fully trained, limiting its feature representation capability. Among all the compared methods, the proposed FMFVNet model achieves the highest recognition accuracy. On the FV-USM dataset, FMFVNet achieves an accuracy of 99.80%, surpassing Swin-T by 1.22%. On the SDUMLA-HMT dataset, it reaches an accuracy of 99.06%, which is 0.7% higher than that of MobileNetV2. These results demonstrate that the proposed model has superior feature extraction capability, meaning it can more precisely capture and learn deep features and subtle variations in finger vein images. This suggests that FMFVNet holds more reliable application potential in finger vein recognition systems. We used the number of model parameters (Params) as a metric to assess model complexity. The number of parameters refers to the total count of trainable parameters in a deep learning model, including weights and biases. It reflects the scale and complexity of the model and serves as an important indicator of resource consumption and computational requirements.