# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 5 Conclusions  
Due to the small feature area of facial expressions, the similarities and differences between feature changes are quite different, so the accuracy of facial expression recognition in natural environment is not high. Therefore, this paper proposes an attentional expression recognition network based on channel weighting. The network constructs a multi-channel structure, aiming to use the auxiliary network to jointly optimize the overall network to improve the convergence speed and recognition accuracy of the network. Firstly, feature extraction and feature fusion are carried out through the subject network. Secondly, the channel weighting module is designed and added, and the auxiliary branch fusion weighting module is used to improve the feature information extraction of the subject network. Finally, the attention module is designed and modified, and the weight of the facial features is increased to achieve the purpose of improving the recognition accuracy and reducing the number of redundant calculations. After the performance verification of each module, and the comparison test and visual analysis of the overall algorithm, the results show that the facial expression recognition method proposed in this paper achieves better recognition accuracy than the existing algorithms. Our method achieved a recognition accuracy of 73.81% on the FER2013 dataset. dataset, and 89.65% and 85.24% on the Oulu_CASIA and RAF-DB datasets, respectively. Due to the completeness of facial feature extraction, the model's processing ability for facial expression recognition in real environments has been enhanced, and its performance has been improved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Oulu** & \\
**Method** & **Accuracy** \\ \hline DTAGN [34] & 81.46\% \\ VGG finetune [35] & 83.26\% \\ PPDN [36] & 84.59\% \\ FaceNet2 ExpNet [37] & 87.71\% \\ MFE (This paper) & 88.34\% \\ MAWNet (This paper) & 89.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of different methods on the Oulu dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**RAF-DB** & \\
**Method** & **Accuracy** \\ \hline DLP-CNN [38] & 84.13\% \\ Base & 84.29\% \\ MFE (This paper) & 84.52\% \\ MAWNet (This paper) & 85.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of different methods on the RAF-DB dataset.  
However, when recognizing facial expressions in this paper, due to occlusion and insufficient image data of different pose types, the model failed to learn more feature information, which also greatly affected the recognition performance. In future work, we will focus on such problems, and conduct research on the direction of joint optimization to obtain a more superior network model.  
Conceptualization, X.L. and H.Z.; methodology, X.L. and Q.Z.; software, X.L. and X.H.; validation, X.L., H.Z. and Q.Z., formal analysis, X.L.; investigation, X.H.; resources, H.Z.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, Q.Z.; visualization, X.L. and X.H.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61872304.  
Not applicable.  
The authors declare no conflict of interest.