# Classroom Expression Recognition Based on Deep Learning
## 5 Conclusions  
The classroom is the main place for school education, so it has always been the focus of educational research. Teachers can grasp the emotional state of students in classroom teaching and adjust teaching methods in time. This standardized feedback mechanism can effectively improve the state that is difficult to achieve in time in traditional teaching. Understanding the shortcomings of students' emotions can increase the interactivity and attention in the classroom. It has shown significant advantages in improving students' attention, enhancing classroom participation and personalized teaching support, thereby promoting the overall improvement in teaching quality. This paper addresses the task of recognizing students' classroom expressions and presents the following contributions to tackle the issue of sample uncertainty in this domain:  
1. Construction of a Student Classroom Expression Dataset: This paper constructs a dataset of student classroom expressions containing five types of emotions: resistance, confusion, understanding, affection, and neutrality. By recording 30 real classroom videos and going through multiple steps such as data collection, cleaning, and annotation, we finally obtained a dataset containing 12,174 images. 2. Development of a Face Data Enhancement Model Based on Generative Adversarial Networks: In order to solve the problems of insufficient samples and category imbalance, this paper proposes a face data enhancement method based on the StarGAN network and further improves the quality and accuracy of image generation by introducing the MultiResUNet model and reconstructing the damage function. 3. Multi-Attention Fusion Network Algorithm (MAF-ER): This paper proposes an expression recognition algorithm MAF-ER that combines spatial, channel, and self-attention mechanisms. By combining the attention mechanism, it focuses on a variety of background noises and related features, improves the accuracy of facial expres \begin{table}
\begin{tabular}{c c} \hline \hline
**Modules** & **Accuracy** \\ \hline CA & 81.37\% \\ SA & 82.06\% \\ SFA & 83.64\% \\ CSA & 84.69\% \\ MLA & 88.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation experiments of MAF-ER on the CED dataset. sion feature extraction, and solves the problem of inaccurate labeling caused by sample uncertainty. With the continuous advancement of recognition technology and deep learning algorithms, classroom expression recognition systems will become more accurate and efficient. This technology can not only integrate multimodal data such as expression, voice, and action but also provide more comprehensive feedback teaching, further supporting the realization of smart classrooms and personalized education. However, in the actual application process, we must face up to the ethical challenges brought by this technology. First, students' emotional data involves personal privacy, and data protection regulations must be strictly observed to ensure the security and privacy of information. There are differences in data protection and privacy regulations, so the design and implementation of the system must strictly comply with local laws and ethical norms. In addition, the application of emotion recognition systems needs to prevent the erroneous consequences caused by mislabeling, especially in terms of the accuracy and fairness of emotion recognition. Therefore, future research should further improve the algorithm model to enhance its robustness and generalization ability. In the future, classroom emotion recognition technology has the potential to be further expanded to a wider range of application scenarios, such as student behavior analysis, automatic adjustment of teaching content, etc., to promote educational transformation and data-driven changes. In the process of development, we must continue to pay attention to the ethical impact of technology implementation and ensure respect for students' privacy and basic human rights.