## 5 Conclusions  
In this study, we proposed a multi-person facial expression recognition system. Our approach aimed to improve the limitation of sparse data and the restriction to single-person facial images. Leveraging the attribute of Haar cascade classifiers, we identified facial regions while simultaneously focusing on the face during subsequent model training. This attention to facial regions rather than the background aided in more precise feature extraction. Additionally, the multi-face detection capability of the Haar cascade classifiers helped us overcome the constraint of single-person facial images. Subsequently, we employed a convolutional neural network (CNN) as the backbone network to implement facial feature extraction and create an efficient classification system. Furthermore, we implemented experiments to optimize the model and confirmed that the model achieved its 69.47% accuracy at epoch 60 when using data augmentation with horizontal flipping. When compared to other methods, our approach outperformed most of them, which demonstrated the effectiveness of our method. In a multi-person expression recognition experiment, the proposed approach achieved an accuracy of 55.88%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** &
\begin{tabular}{c} **Real-Time** \\ **Multiple Faces** \\ \end{tabular} & **O(\(\cdot\))** \\ \hline Baseline (CNN) & 60.60\% & - & O(2.1 \(\times\)\(10^{8}\)) \\ \hline [PERSON] et al. [3] model 1 & 65.77\% & - & O(3.8 \(\times\)\(10^{9}\)) \\ \hline [PERSON] et al. [3] model 2 & 65.23\% & - & O(3.7 \(\times\)\(10^{8}\)) \\ \hline [PERSON] et al. [9] & 70.02\% & - & O(2.7 \(\times\)\(10^{5}\)) \\ \hline Our (CNN + Haar) & 69.47\% & + & O(2.1 \(\times\)\(10^{8}\)) + O(1.5 \(\times\)\(10^{5}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results in comparison with those of other models.  
Figure 12: An example of the proposed model for multi-person expression recognition.  
However, our system has its limitations. Firstly, to achieve higher accuracy, we employed methods with lower time complexity, such as the Haar cascade classifier, but we did not reduce the time complexity by altering the architecture of the CNN model. As a result, compared to the approach of modifying the CNN model by [PERSON] et al. [9], we slightly lagged in both accuracy and time complexity. In future work, we want to incorporate the concept of attention, allowing the model to focus more on learning emotion-related features. We plan to expand the training dataset for multi-person expression recognition and develop more algorithms tailored specifically for this task to achieve higher accuracy.  
All authors contributed to the design and implementation of the research, to the analysis of the state of the art, and the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
National Science and Technology Council: 111-2221-E-415-012-MY3.  
Not applicable.  
[1]**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://paperswithcode.com/dataset/fer2013](https://paperswithcode.com/dataset/fer2013)]([https://paperswithcode.com/dataset/fer2013](https://paperswithcode.com/dataset/fer2013)); accessed on 26 November 2023.  
The authors declare no conflict of interest.