## 3 Methodologies
### Multi-Channel Emotion Recognition Algorithm for the Classroom
#### 3.2.2 Multi-Channel Emotion Recognition Network Architecture  
In this study, we aim to develop a Multi-Channel Emotion Recognition Network (MultiEmoNet) utilizing the EfficientNetv2-S model. This model is characterized by its lightweight design and high accuracy, making it suitable for feature extraction [31]. The primary focus of our network will be facial expressions, while background information and skeletal data will serve as supplementary inputs. A detailed description of the MultiEmoNet architecture is provided in the following section. Face Extraction Module  
In image classification tasks, the expressions of the same individual exhibit greater similarity than identical expressions across different individuals. This phenomenon complicates the differentiation between various categories of facial expressions. To address this challenge, the present study incorporates a CLF within the feature extraction module to optimize both intra-class and inter-class variations. The implementation of Center Loss enhances the accuracy of facial expression classification by minimizing intra-class variation while indirectly facilitating inter-class separation. The formulation for Center Loss is presented in Equation (9). \[\mathrm{L_{c}}=\frac{1}{2}\sum_{i=1}^{m}\left\|\mathrm{x_{i}}-\mathrm{c_{yi} }\right\|_{2}^{2} \tag{9}\]  
where m represents the batch size, \(\mathrm{x_{i}}\) denotes the depth feature of sample i, and \(\mathrm{c_{yi}}\) indicates the feature center of category \(\mathrm{y_{i}}\), to which sample i belongs. The term \(\left\|\mathrm{x_{i}}-\mathrm{c_{yi}}\right\|_{2}\) signifies their L2 norm. To enhance the classification performance, center loss is typically employed in conjunction with the cross-entropy loss function, resulting in a joint optimization framework (Equation (10)). \[\mathrm{L}=\mathrm{L_{CE}}+\lambda\mathrm{L_{c}} \tag{10}\]  
where \(\mathrm{L_{CE}}\) represents the cross-entropy loss, and \(\lambda\) serves as a balancing parameter to regulate the relative importance of the two losses. Figure 6 illustrates the results of feature visualization on the MNIST dataset, trained using both center loss and cross-entropy loss. The effective application of center loss enhances class separability by optimizing inter-class margins while minimizing intra-class variation. It has been demonstrated that a pre-trained convolutional neural network utilizing the VGGface2 dataset exhibits a superior performance in face emotion recognition [32]. Consequently, this study employs the pre-trained EfficientNetv2-S model as the backbone network for face feature extraction, while removing its final fully connected layer to facilitate multi-channel information fusion tasks. To further enhance the emotion recognition efficacy, an Auxiliary Attention Module (AAM) is incorporated following the feature ex  
Figure 6: Feature comparison of the MINIST dataset trained with different loss functions. (**a**) Center Loss; (**b**) Cross Entropy Loss. traction network. This module comprises both spatial attention and channel attention components (Figure 7). The Spatial Attention Module captures spatial features across various orientations through dimensionality-reducing convolutions and a series of specialized convolutions (Conv3 \(\times\) 3, Conv1 \(\times\) 3, and Conv3 \(\times\) 1). The resulting spatial attention maps are then multiplied with the original feature maps to amplify responses at critical spatial locations. The Channel Attention Module compresses spatial dimensions via global average pooling and learns the importance weights for each channel through a sequence of linear transformations. These weights are normalized using a sigmoid function and subsequently multiplied with the original features to enhance the capture of significant channels. By integrating these two modules, AAM effectively identifies essential spatial and channel characteristics. The instantiation of multiple AAM modules enables simultaneous focus on different facial regions, thereby improving the accuracy in emotion recognition.