## 4 Experiments
### Analysis of Experimental Results  
#### 4.5.1 Confusion Matrices  
In order to analyze the recognition accuracy of BGA-Net for various types of expressions, the confusion matrices on the Fer2013, CK+, and JAFFE datasets were plotted. In the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Model** & **CK+** & **JAFFE** & **Fer2013** \\ \hline CNN & 94.39\% & 91.42\% & 65.68\% \\ CNN+SWC & 94.89\% & 93.45\% & 66.74\% \\ CNN+SWC+RNN & 95.14\% & 94.34\% & 68.72\% \\ CNN+SWC+LSTM & 94.52\% & 94.33\% & 68.92\% \\ CNN+SWC+GRU & 95.23\% & 94.58\% & 69.43\% \\ CNN+SWC+BiGRU & 95.54\% & 95.23\% & 70.62\% \\
**BGA-Net** & **96.36 \%** & **95.51\%** & **71.89\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of each module in our model on the three datasets. ’SWC’ refers to sliding window cropping strategy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stride** & **Scale** & **Accuracy\%** \\ \hline
2 & 3 & 70.44 \\
2 & 5 & 70.33 \\
2 & 7 & **71.89** \\
2 & 9 & 70.10 \\
2 & 11 & 71.02 \\ \hline
3 & 3 & 70.08 \\
3 & 5 & 70.38 \\
3 & 7 & 70.80 \\
3 & 9 & 70.38 \\
3 & 11 & 70.99 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of sliding window cropping.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\gamma\) & **0** & **0.5** & **1** & **2** & **5** \\ \hline Accuracy\% & 70.74 & **71.89** & 70.94 & 70.41 & 68.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The value of \(\gamma\).  
confusion matrix, the higher the percentage corresponding to the elements on the diagonal, the better the recognition accuracy of the method in the corresponding category.  
Based on the confusion matrix of Fer2013 shown in Figure 5(a), it can be observed that the recognition rates for the categories of anger, fear, and sadness are relatively lower, while the rates for the categories of happiness and surprise are higher. This phenomenon can be attributed to the fact that, compared to other categories, the facial features in happiness and surprise expressions are more prominent. For instance, in a state of happiness, there are often distinct features such as upturned corners of the mouth and raised eyebrows, while surprise expressions exhibit similar characteristics like widened eyes. On the other hand, anger, fear, and sadness share certain common features, such as open mouths and tense facial expressions. Among these three categories, fear expressions bear more similarities to the other categories, which results in the lowest recognition rate for fear expressions among all categories.  
The confusion matrices in Figure 5(b),c represent the CK+ dataset and JAFFE dataset, respectively. It can be observed that emotions such as happiness, surprise, and disgust have relatively higher recognition rates, while anger, fear, sadness, and contempt have lower recognition rates. The substantial difference in recognition accuracy compared to the FER2013 dataset is primarily attributed to the data collection process of these two datasets. Both CK+ and JAFFE datasets are captured in controlled laboratory settings with minimal interference from factors such as occlusion, lighting variations, and human-related disturbances. Consequently, the image quality is higher, making the emotions more discernible and easier to recognize.