# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 5 Conclusions  
To balance the trade-off between recognition accuracy and efficiency in finger vein recognition systems, this paper proposes a more efficient finger vein recognition network model--Faster Multi-Scale Finger Vein Network (FMFVNet). By integrating a lightweight design with the Multi-Scale Convolutional Attention (MSCA) module, FMFVNet significantly enhances computational efficiency while maintaining recognition accuracy, making it more suitable for real-world applications. FMFVNet employs FasterNet Block as the primary feature extraction network, utilizing efficient Partial Convolution (PConv) to optimize model complexity and computational efficiency, ensuring fast inference capability. The combination of multi-scale convolution and attention mechanisms in MSCA strengthens the model's ability to capture vein vessel features, significantly improving its adaptability and robustness. Experimental results on the FV-USM and SDUMLA-HMT datasets demonstrate that FMFVNet achieves a higher recognition accuracy and lower inference time compared to existing models, validating its feasibility for finger vein recognition. Furthermore, the ablation study results confirm that the MSCA module effectively improves the recognition accuracy, providing a viable solution for finger vein detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Inference Time (ms)** \\ \hline MobileNetV2 & 2.21 \\ EfficientNet-B0 & 3.02 \\ ResNet-50 & 2.63 \\ Swin-T & 4.69 \\ FMFVNet & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of inference time across different models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**MSCA**} & \multicolumn{3}{c}{**ACC (\%)**} \\ \cline{2-3}  & **FV-USM** & **SDUMLA-HMT** \\ \hline – & 99.19 & 98.36 \\ ✓ & 99.80 & 99.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model accuracy on datasets with and without MSCA.  
Although the proposed FMFVNet method demonstrates a high recognition accuracy and computational efficiency in finger vein recognition tasks, it still has certain limitations. First, the experiments in this study were conducted on high-performance GPUs, whereas real-world applications often involve embedded devices with limited computational capacity. Therefore, the suitability of this method in embedded environments requires further verification. Therefore, future research will explore techniques such as model pruning, quantization, and knowledge distillation to reduce computational costs and enhance the applicability of the model in embedded environments. Second, the input data used in this study underwent ROI extraction. The robustness of the proposed method for raw finger vein images without ROI extraction still needs further investigation. Future work will consider directly recognizing unprocessed finger vein images, enabling the network to learn effective vein features directly from raw images. This approach aims to reduce image preprocessing steps, further enhancing the system's real-time performance and practical application convenience.  
Conceptualization, Z.Z.; Methodology, Z.Z.; Software, Z.Z.; Validation, Z.Z.; Resources, C.S.; Writing--original draft, Z.Z. and C.S.; Writing--review & editing, P.L.; Project administration, P.L. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are publicly available in the FV-USM and SDUMA-HMT, reference number [18; 39].  
The authors declare no conflicts of interest.