# CFormerFaceNet: Efficient Lightweight Network Merging a CNN and Transformer for Face Recognition
## 5 Conclusions  
In this paper, we proposed the CFormerFaceNet model, an efficient deep neural network for face recognition on embedded or mobile devices, which combines the advantages of both CNN and Transformer architectures. The model can significantly reduce the amount of computation and improve computational efficiency while maintaining competitive performance. Experiments were conducted on multiple face recognition test datasets to verify the effectiveness of the proposed method. The results showed that the proposed CFormerFaceNet model exhibited competitive accuracy while significantly reducing computational cost compared with other state-of-the-art lightweight face recognition deep neural networks. Additionally, experiments were carried out using computers and embedded devices, and the results showed that our proposed CFormerFaceNet model can complete face recognition tasks in real time while maintaining accuracy. This has practical value for real-time and mobile face recognition applications. In future work, we will design more efficient lightweight modules from the perspective of hardware system resources and leverage the high parallelism of FPGAs or AI chips to further speed up the inference of our model in edge devices.  
Methodology, L.H. ([PERSON]); software, L.H. ([PERSON]); validation, L.P.; formal analysis, L.H. ([PERSON]); investigation, L.H. ([PERSON]); resources, L.P.; writing--original draft preparation, L.H. ([PERSON]); writing--review and editing, L.P.; supervision, L.H. ([PERSON]); project administration, L.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Platform** & **112 \(\times\) 112** & **224 \(\times\) 224** & **300 \(\times\) 300** & **448 \(\times\) 448** & **800 \(\times\) 800** \\ \hline Laptop with RTX3050 Ti GPU & 105.02 fps & 90.48 fps & 61.89 fps & 37.59 fps & 12.47 fps \\ Laptop with RS-5600H CPU & 45.86 fps & 12.18 fps & 6.56 fps & 2.87 fps & 0.92 fps \\ Jetson Nano & 7.48 fps & 4.64 fps & 3.14 fps & 1.52 fps & 0.50 fps \\ \hline \hline \end{tabular}
\end{table}
Table 6: Running speed of CFormerFaceNet using different input face image resolutions on laptops and a Jetson Nano development board.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **PARAMs** & **FLOPs** & **Speed** \\ \hline MobileFaceNet & 1.0 M & 0.45 G & 4.86 s \\ CformerFaceNet (ours) & 1.7 M & 0.04 G & 3.98 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speed comparison of CFormerFaceNet and MobileFaceNet on the embedded device.  
**Funding:** This research was funded by the key research and development program of Shaanxi Province, China (Grant No. 2022 GY-134); the Science and Technology Foundation of Xi'an University of Architecture and Technology, China (Grant No. ZR19059); and the Special Scientific Research Project of the Education Department of the Shaanxi Provincial Government of China (Grant No. 21 JK0732).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.