## 4 Experimental Results
### Comparison with Other Models  
In this section, we compare with the baseline (CNN) the models proposed by [PERSON] [3] and [PERSON] [25]. Accuracy and time complexity were adopted as the evaluation metrics, and FER-2013 was used as the training and test data. First, we compared the baseline with our model. As shown in Table 6, while the addition of an extra convolutional layer and the Haar cascade classifier increased the time complexity of our model, it also resulted in a significant improvement of 8.87% in accuracy. Therefore, we considered this trade-off acceptable. This improvement was evident as the baseline extracted images that included the background, resulting in the inclusion of a considerable amount of noise in the features. Moreover, the limited size of the dataset did not provide sufficient support for the model to converge completely, which led to the model's inferior performance. The two models proposed by [PERSON] achieved higher accuracy compared to that of the original CNN by adjusting the composition of layers and hyperparameters. However, blindly deepening the model sometimes failed to extract useful facial features. Additionally, these models tended to capture noise features from the background during feature extraction, resulting in a 3.7% lower accuracy compared to that of our model. Additionally, while [PERSON] achieved a lightweight model in Model 2 by modifying the number of layers and hyperparameters, this resulted in a reduction in accuracy. Furthermore, in terms of time complexity, their approach slightly lags behind our system's overall efficiency. We also compared the model proposed by [PERSON] et al. [3] with our model. [PERSON] et al. proposed a two-branch convolution network system. A branch was defined as an attention branch. The attention branch paid more attention to emotionally relevant facial regions of the images than our Haar cascade classifier did. Therefore, they achieved higher accuracy than we did, and their two branches significantly reduced the time cost incurred by the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Epoch** & **Accuracy** \\ \hline
30 & 68.36\% \\ \hline
40 & 69.08\% \\ \hline
50 & 69.13\% \\ \hline
60 & **69.47\%** \\ \hline
75 & 69.14\% \\ \hline \hline \end{tabular} The optimal results are highlighted in bold.  
\end{table}
Table 4: Epoch optimization result.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **O(\(\cdot\))** \\ \hline CNN + Haar & \multirow{2}{*}{69.47\%} & \multirow{2}{*}{76.28\%} & \multirow{2}{*}{69.45\%} & \multirow{2}{*}{72.70\%} & \multirow{2}{*}{O(\(2.1\times 10^{8}\)) + O(\(1.5\times 10^{5}\))} \\ + Augmentation & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Other evaluation metrics with an optimization model.  
deep model. Although our model fell behind [PERSON]'s model in terms of both accuracy and time complexity, our model successfully achieved real-time multi-person expression recognition. Real-time multi-person emotion recognition remains essential for applications such as monitoring classroom attentiveness. Despite the trade-offs, our model addresses the necessity of real-time multi-person emotion recognition in specific applications.