## 2 Related Work
### Current Status of Emotion Recognition Technology  
Emotional recognition determines the emotional state of an individual by analyzing their behavior, mental state, or physiological signals [13]. [PERSON]'s study revealed that facial expressions account for 55% of emotional information, intonation for 38%, and speech words for only 7% [14]. This has led to the mainstream of facial expressions in emotion recognition, prompting numerous studies to extract emotions from facial features. For example, [PERSON] et al. [15] used CNN to detect different types of facial expressions. However, there are limitations in emotion recognition for individual facial expressions, and combining other information sources, such as body gestures and background scenes, has emerged as a new research direction.  
Researchers have also verified the importance of body posture in emotional expression. [PERSON] [16] demonstrated the strong association between posture and emotion by analyzing joint movements in static postures. [PERSON] [17] further combined neural networks and body posture for emotion analysis to improve the accuracy of recognition. Integrating a multi-channel model of facial expression and limb posture allows for a more comprehensive reflection of an individual's emotional state.  
Scene contextual information also plays a key role in emotion recognition. The accuracy of emotion recognition significantly improves when facial expressions are consistent with the scene [18]. [PERSON] et al. [19] proposed a deep learning-based context-aware emotion recognition method that fuses character and scene features to improve the classification accuracy. [PERSON] et al. [20], on the other hand, utilized graph convolutional networks (GCNs) to encode contextual information and enhance the robustness of emotion recognition. [PERSON] et al.'s [21] contextual causal intervention module (CCIM) further improves the performance of the context-aware model by reducing the scene context bias.  
In classroom contexts, emotion recognition techniques help teachers adjust teaching strategies based on students' emotional feedback. However, classroom emotion recognition faces challenges such as the scarcity of datasets, occlusion effects, and annotation accuracy. For this reason, researchers have introduced deep learning techniques to improve the accuracy of classroom emotion recognition. [PERSON] et al. [22] employed a CNN and transfer learning techniques to identify and categorize students' facial emotions in classroom  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Emotion Categories** & **No** & **Emotion Categories** \\ \hline
1 & Affection & 14 & Excitement \\
2 & Anger & 15 & Fatigue \\
3 & Annoyance & 16 & Fear \\
4 & Anticipation & 17 & Happiness \\
5 & Aversion & 18 & Pain \\
6 & Confidence & 19 & Peace \\
7 & Disapproval & 20 & Pleasure \\
8 & Disconnection & 21 & Sadness \\
9 & Disquietment & 22 & Sensitivity \\
10 & Doubt/Confusion & 23 & Suffering \\
11 & Embarrassment & 24 & Surprise \\
12 & Engagement & 25 & Sympathy \\
13 & Esteem & 26 & Yearning \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of 26 distinct emotions [12].  
settings, utilizing the VGG16 model. [PERSON] et al. (2020) created a spatio-temporal residual attention network (STRAN)-based model to pull out features of students' facial expressions. They were able to achieve an 80.45% recognition rate on a dataset of students' facial expressions that they made themselves.  
These studies have shown that combining multiple information sources can improve the comprehensiveness and accuracy of emotion recognition. However, current classroom emotion recognition still primarily relies on facial expressions ([PERSON] et al., 2019), and the changing light and occlusion in classroom environments easily obscure facial features, thereby affecting its accuracy and robustness ([PERSON] et al., 2019).