# Update class center using momentum-based smoothing
## 3 Experiments and Results
### Experimental Results and Analysis  
#### 3.3.1 Experimental Results on the Dataset  
To verify the effectiveness of the proposed LightExNet algorithm, experiments were conducted on the FER2013, CK+, and RAF-DB datasets, and the results were visualized. * Experimental results on the Fer2013 dataset  
Figure 11 shows the training accuracy curve obtained by the model on the Fer2013 dataset. By analyzing the picture, it can be concluded that the curve as a whole is rising as the number of iterations stacks up, which means that the accuracy of the training is constantly improving, and continuing the analysis, it can be seen that the training accuracy improves the fastest at the very beginning of the training phase, i.e., before the 21 st generation. When training proceeds to about generation 35, the curve rise begins to slow down and the slope begins to gradually decrease. However, at around generation 45, the slope of the curve starts to grow again, and the curve starts to rise faster, and then slows down at generation 70, and then after generation 110, the curve becomes smooth, and the accuracy of the training no longer changes significantly, until it finally converges. Figure 11: Fer2013 training accuracy curve. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Hyperparameter Name**} & **FER2013 and** & \multirow{2}{*}{**CK+ Dataset**} \\  & **RAF-DB Dataset** & \\ \hline Batch size & 32 & 16 \\ \hline Epochs & 150 & 150 \\ \hline Initial learning rate & 0.001 & 0.0001 \\ \hline \multirow{3}{*}{Learning rate decay} & \multicolumn{2}{c}{Cosine annealing with} & \multicolumn{2}{c}{Cosine annealing with} \\  & minimum learning rate set to & minimum learning rate set to \\  & \(1/100\) of initial learning rate & \(1/100\) of initial learning rate \\ \hline Optimizer & AdamW & AdamW \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter setting. Figure 12 shows the test accuracy curve. By analyzing the figure, it can be observed that, similar to the training curve, the test accuracy also increases overall with the number of iterations. The most significant improvement occurs during the early training phase, specifically within the first 21 epochs. However, the overall direction of the curve is gradually stabilized, the slope of the curve gradually decreases, and the improvement of the accuracy also slows down gradually, until it eventually stabilizes and reaches the convergence state. The confusion matrix of the Fer2013 test set is shown in Figure 13.  
It is easy to see from Fer2013 confusion matrix results (Figure 13) that the method proposed in this paper has a higher degree of recognition for the two types of expression, Happy and Surprised, compared with other expressions, with an accuracy of 0.89 and 0.87,  
Figure 12: Fer2013 testing accuracy curve. Figure 13: Fer2013 confusion matrix results. The confusion matrix on the CK+ facial expression dataset reveals the detailed recognition performance across seven emotion categories. Overall, the model achieves an impressive accuracy of 97.366%, indicating its strong capability in facial expression recognition tasks. Among all categories, Happy and Surprise expressions are recognized with perfect accuracy (1.00), Disgust and Fear achieve high recognition rates of 0.98 and 0.96, respectively, with minimal confusion. Angry achieves a recognition rate of 0.94, with a few samples misclassified as Sad and Contempt. Sad obtains a slightly lower accuracy of 0.92, and is most frequently confused with Contempt (0.05). The most challenging category to classify is Contempt, which exhibits the lowest recognition rate of 0.87. It is often misidentified as Sad (0.09) or Surprise (0.04). * Experimental results on RAF-DB dataset  
Figure 17 shows the training accuracy curve obtained by the model on the RAF-DB dataset, Figure 18 shows the test accuracy curve obtained by the model on the RAF-DB dataset, and Figure 19 shows the confusion matrix for the RAF-DB test set.