# Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention
## 4 Experiment
### Ablation Experiments  
In order to fully investigate the effectiveness of each module of DPSAN, an ablation experiment is designed in this paper to verify the effects of two modules, the dual path feature fusion module and the SA module, on FER. The baseline model global path network (GN) is designed in the experiment. This model only takes the global image as input and generates a feature map through a ResNet18 shown in Figure 3, and then classifies the expressions via a classifier. In order to demonstrate the advantages of the dual path feature fusion module and the SA module, two groups of comparative experiments are designed; the comparison between the dual path network (DPN) and GN, and the comparison between DPN and DPSAN. First, to demonstrate that dual path networks can reduce model overfitting and have better accuracy stability over different data, a DPN model is experimentally designed. The DPN model has one more local path than the GN model, which takes the global image and local image blocks as input and is compared with GN for both accuracy and standard deviation. Second, to demonstrate the advantages of the SA module, DPN is compared with DPSAN, in which DPN lacks the stacked attention module. By comparing the two, the superiority of DPSAN in both accuracy and standard deviation can be demonstrated. The histogram of the comparison between the two groups of experiments is shown in Figure 10. We annotated the experimental data of DPSAN. From Figure 10, it can be roughly seen that the accuracy of GN fluctuates a lot in 10-fold cross-validation, while DPN fluctuates less, indicating that it is less stable than DPN; and at the same time, the advantage of accuracy is too small. The performance of DPSAN clearly exceeds that of the baseline, i.e., GN. The experimental results are shown in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Acc (\%)** \\ \hline TFE-JL [58] & 84.3 \\ SHCNN [57] & 86.5 \\ ResNet + FC [59] & 83.4 \\ VGG16 + PLD [21] & 84.99 \\ ESR-9 [14] & 87.15 \\
**DPSAN (Ours)** & **87.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of DPSAN with other models on the FERPLUS dataset. Figure 9: Confusion matrix for DPSAN prediction based on FERPLUS dataset and sentiment label distribution. **(1) DPN VS. GN:** DPN and GN were compared in the experiments to demonstrate the benefits of adding a local path. The combined results in Figure 10 and Table 4 can now show that GN performs slightly better than DPN in terms of accuracy in certain folds. However, the standard deviation calculated by DPN is much smaller than the results of GN, which is more stable than GN, and which also indicates that DPN can introduce multi-scale expression details by adding local paths in facial expression recognition through region segmentation, and that it can complement the detailed attributes ignored by the global image features and effectively avoid overfitting. The reduction in the standard deviation shows that combining local details and global feature mapping can well suppress intra-class differences. Additionally, considering the different contributions of local regions and global images to the FER problem, these features need to be re-weighted. Here, we design the stacked attention module. **(2) DPSAN VS. DPN:** Experiments were conducted to compare DPSAN and DPN to validate the benefits of overlaying the attention module. As shown in Table 4, the accuracy of DPSAN improved by 4.03% on the CK+ dataset. As shown in Figure 10, DPSAN outperforms DPN and GN in almost all folds. The comparative experimental data in Table 4 show that the SA module in DPSAN can effectively reassign the weights of each feature map based on its contribution to FER. Through two sets of ablation experiments, the following conclusions can be drawn. First, it can be demonstrated that the dual path feature fusion module in the DPSAN model is effective and can effectively suppress the intra-class variance, but it leads to a slight decrease in recognition accuracy.