# Classroom Expression Recognition Based on Deep Learning
## 3 Theoretical Framework of Facial Expression Recognition
### Classroom Expression CED Dataset  
With the advancement of expression recognition technology, scholars have increasingly recognized the importance of high-quality expression data for developing effective neural network models and analyzing learning conditions. Currently, researchers both domestically and internationally have established various student expression datasets, though these datasets differ in scale and construction methods. Student expression datasets can be classified into three types: the first type is based on the six basic emotions proposed by [PERSON], as shown in Table 2; the second type is built using the facial action coding system; the third type is established according to specific research needs, analyzing the requirements to create an expression dataset suitable for the study at hand.  
Figure 3: System architecture design diagram.  
To facilitate research and applications of student expression recognition, this paper established a dataset in a classroom environment (CED). The CED dataset contains a large collection of facial expression images. Based on a literature review, this paper identified the expression categories to be collected, as shown in Table 3. The specific steps are as follows:  
1. Data Collection: Camera equipment was used to capture pictures and videos of student expressions in the classroom environment, collecting a variety of expressions such as resistance, confusion, fatigue, and more.
2. Data Cleaning: The collected images or videos were screened and edited to ensure consistency and usability in image quality.
3. Data Labeling: Each image was labeled to indicate the facial expression category corresponding to the face in the image.
4. Dataset Partitioning: The dataset was divided into training, validation, and test sets for model training, evaluation, and parameter tuning.
5. Data Cleaning: During dataset creation, checks were conducted for missing values, outliers, duplicate values, and other irregularities, and appropriate cleaning procedures were applied.
6. Exporting the Dataset: The dataset was exported in jpg format.  
To advance research on student classroom expression recognition and address the issues of inconsistent quality in current student expression datasets and the challenge of achieving standardized category divisions, this paper recorded 30 real-world classroom videos from colleges to construct a dataset. By reviewing and comparing literature in related fields, analyzing college classroom environments, and consulting experts, this study categorizes and organizes existing student classroom expressions. The classification of the dataset is outlined in Table 3. Some examples of facial expressions in the dataset are shown in Figure 4. Through statistical analysis, expression categories that frequently occur in the classroom and positively influence classroom evaluation were identified. Ultimately, a student classroom expression dataset was constructed, containing five categories--resistance, confusion, understanding, fatigue, and neutrality--comprising a total of 12,174 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Researchers** & **Expression Type** & **Is it Spontaneous** & **Basic Tags** & **Type** \\ \hline [PERSON] [24] & 5 & Yes & Listen, doubt, understand, resist & Category 3 \\ [PERSON] et al. [25] & 6 & Yes & Happy, Sad, Disgusted, Fearful, & Category 2 \\ [PERSON] et al. [26] & 7 & No & Normal, Happy, Angry, Sad, & Category 3 \\ \hline \end{tabular}
\end{table}
Table 2: Student expression dataset.  
\begin{table}
\begin{tabular}{c c c} \hline
**Label** & **Mood** & **Expression Characteristics** \\ \hline
0 & Resistance &
\begin{tabular}{c} \\ \end{tabular} \\
1 & Confusion &
\begin{tabular}{c} \\ \end{tabular} \\
2 & Understanding &
\begin{tabular}{c} Prominent cheekbones, raised upper lip \\ Open mouth, protruding lips, eyes closed \\ \end{tabular} \\
3 &
\begin{tabular}{c} \\ \end{tabular} \\
4 & Neutral & Relaxed expression, no obvious changes \\ \hline \end{tabular}
\end{table}
Table 3: Classroom emotion classification.