## 4 Experiments
### Experimental Results
#### 4.3.3 Results of a Classroom Multichannel Emotion Recognition Experiment  
This study conducts experiments on the classroom emotion dataset to verify the effectiveness of the MultiEmoNet network structure. We refer to the background, face, and skeletal information as Bg, Face, and Sk, respectively. Table 9 shows the experimental results of this study using single-face information. In this study, the FaceFeatXtractor network outperforms other algorithms such as Resnet50, Vgg16, MobileNetv3, and EfficientNetv2-S, achieving an accuracy of 0.721. This paper's proposed face feature extraction network effectively enhances the accuracy of emotion recognition. Figure 18: Comparison of object detection. (**a**) Classroom A; (**b**) Classroom B. \begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline YOLOv8x-COCO & 0.744 & 0.698 & 0.77 & 0.479 \\ YOLOv8x & 0.781 & 0.831 & 0.851 & 0.566 \\ YOLOv8x-Improved & 0.802 & 0.852 & 0.863 & 0.572 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of custom classroom object detection dataset results. Table 10 shows the results of experiments using single-search information in this study. The experiments compare the accuracy of emotion classification using traditional machine learning methods, such as SVM, KNN, and Random Forest, with the use of preprocessed-generated skeletal information maps fed into a convolutional neural network (EfficientNetv2-S). The results demonstrate that the method presented in this paper performs better on skeletal information when compared to traditional machine learning methods. Table 11 shows the experimental results of this study using single background information. The experimental results show that the MultiEmoNet model combining multi-channel information achieves the highest emotion classification accuracy. The model that uses skeleton information (Sk) and face information (Face) as the inputs performs better than the single-channel information model. This shows that the multi-channel emotion recognition method suggested in this paper works. In particular, fusing background information (Bg) significantly improves the model's performance. The reason may be that face information is missing in some images, and the background can make up for it; secondly, the resolution of the face images is too low, and the background information contains more emotional features compared to the background information. In this study, we produce confusion matrices using face and skeletal features (Face + Sk) as well as face, skeletal, and background features (Face + Sk + Bg) to verify this (Figure 19). \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Precision** \\ \hline ResNet50 & 0.794 \\ Vgg16 & 0.774 \\ MobileNetv3 & 0.771 \\ EfficientNetv2-S & 0.813 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: MultiEmoNet provides the experimental results for the use of multi-channel information. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Precision** \\ \hline Face + Sk & 0.841 \\ Face + Sk + Bg & 0.914 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of experiments using single background information. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Precision** \\ \hline ResNet50 & 0.794 \\ Vgg16 & 0.774 \\ MobileNetv3 & 0.771 \\ EfficientNetv2-S & 0.813 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of experiments using multi-channel information. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Precision** \\ \hline VGG16 & 0.678 \\ MobileNetv3 & 0.669 \\ EfficientNetv2-S & 0.702 \\ FaceFeatXtractor & 0.721 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of experiments using single-person face information.