# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 4 Experiments and Results
### Comparative Experiments with Different Models
A larger number of parameters indicates a more complex model, requiring more GPU memory and computational resources during training and inference. Since finger vein recognition models are often deployed on embedded devices or hardware terminals (such as access control systems and mobile payment devices), where hardware performance and resources are typically constrained, models with fewer parameters are better suited for practical applications. Additionally, we measured the floating-point operations (FLOPs), which represent the total number of floating-point computations required for a single forward pass of the model. FLOPs quantify the total computational workload for matrix multiplications, additions, convolutions, and other operations, making them a key metric for evaluating computational complexity. FLOPs are closely related to inference efficiency and computational resource requirements. A \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**ACC (\%)**} \\ \cline{2-3}  & **FV-USM** & **SDUMLA-HMT** \\ \hline MobileNetV2 & 99.19 & 98.36 \\ EfficientNet-B0 & 99.19 & 98.67 \\ ResNet-50 & 99.39 & 98.85 \\ Swin-T & 98.58 & 98.42 \\ LFVRN-CE [30] & 98.58 & 97.74 \\ Coding SA [31] & 99.39 & 96.01 \\ FV-ViT [32] & 99.59 & 94.51 \\ FMFVNet & 99.80 & 99.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of _ACC_ with other methods on FV-USM and SDUMLA-HMT. higher FLOPs count indicates a more computationally intensive model, requiring more processing power. Generally, models with lower FLOPs tend to have faster inference speeds. However, in real-world inference scenarios, multiple additional factors can influence overall model performance. Table 3 presents the comparison of Params and FLOPs across different models. It can be observed that due to differences in architectural design, ResNet-50 and Swin-T have a significantly larger number of parameters than lightweight networks, making them less suitable for deployment in resource-constrained environments. In contrast, MobileNetV2 and EfficientNet-B0 have relatively fewer parameters, making them more suitable for deployment on embedded devices with limited computational resources. The proposed FMFVNet model reduces the number of parameters by 1.71 M compared to EfficientNet-B0, classifying it as a lightweight network while achieving superior accuracy and feature extraction capabilities. This balance between model size and recognition performance makes FMFVNet well-suited for deployment in finger vein recognition systems. Additionally, FMFVNet reduces FLOPs by 27.42 M compared to EfficientNet-B0 and is approximately 11.5 times more efficient than ResNet-50 in terms of floating-point operations, demonstrating a significant advantage in finger vein recognition tasks by balancing efficiency and practicality. However, a higher FLOPs count indicates greater computational complexity, but it does not necessarily correlate directly with actual runtime speed. The real execution speed of a model is influenced by factors such as architectural efficiency, hardware optimizations, and execution pipeline design. Therefore, evaluating model runtime performance should not rely solely on FLOPs as the only reference metric. To evaluate inference performance in real-world scenarios, it is necessary to include inference time per image as part of the model assessment for a more accurate evaluation. Inference time per image typically refers to the duration of the entire forward pass, from the moment the model receives an input to the generation of the output. It serves as a key metric for measuring the performance of deep learning models and their deployment efficiency in practical applications. Table 4 presents the inference time per image for different models. To ensure measurement accuracy, we repeated the inference process on the input tensor 1000 times.