# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 4 Experiment
### Ablation Experiment  
To investigate the contribution of each component within the MAFE framework, ablation experiments were conducted on the RAF-DB and Occlusion-RAFDB datasets. The outcomes of these experiments are presented in Table 3.  
Observing the table, it is evident that compared to using only PTIR-50 or SWIN-E for expression recognition, MAFE achieves good performance on both the original RAF-DB dataset and the Occlusion-RAFDB dataset, with accuracies of 92.11% and 89.42%.  
In order to demonstrate the efficacy of con-feature loss, a comparative analysis was conducted between con-feature loss and feature fusion. The objective was to evaluate the performance of processing \(F_{X}\) and \(F_{H}\) using con-feature loss in comparison to the performance of fusing \(F_{X}\) and \(F_{H}\) with features, as illustrated in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Method** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{RAF-DB} & RAN [15] & 82.72 \\  & EfficientFace [37] & 83.24 \\  & MA-Net [38] & 88.40 \\  & LANet [45] & 86.70 \\  & MAPNet [46] & 87.26 \\  & MFNet [47] & 88.53 \\  & SCAN-CCI [41] & 89.02 \\  & MPA [43] & 89.54 \\  & Latent-OFER [18] & 89.60 \\  & MAFE & 92.11 \\ \hline \multirow{8}{*}{FERPlus} & RAN [15] & 88.55 \\  & SCN [48] & 88.01 \\  & VTFF [39] & 88.81 \\  & ADC-Net [49] & 88.90 \\  & SpResNet-ViT [50] & 88.10 \\  & SCAN-CCI [41] & 89.42 \\  & IPD-FER [51] & 88.42 \\  & MPA [43] & 89.13 \\  & MAFE & 90.15 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison with other methods, using the original datasets.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Method** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{RAF-DB} & PTIR-50 & 91.46 \\  & SWIN-E & 91.30 \\  & PTIR-50+SWIN-E & 91.57 \\  & PTIR-50+Crop & 91.53 \\  & SWIN-E+Crop & 91.37 \\  & MAFE & 92.11 \\ \hline \multirow{8}{*}{Occlusion-RAFDB} & PTIR-50 & 87.09 \\  & SWIN-E & 86.91 \\ \cline{1-1}  & PTIR-50+SWIN-E & 88.76 \\ \cline{1-1}  & PTIR-50+Crop & 88.47 \\ \cline{1-1}  & SWIN-E+Crop & 87.64 \\ \cline{1-1}  & MAFE & 89.42 \\ \hline \end{tabular}
\end{table}
Table 3: Ablation study on MAFE.  
The results of the experiment demonstrate that the method of using con-feature loss to process \(F_{X}\) and \(F_{H}\), thereby enabling the model to learn more precise features, is significantly more accurate than the method of inputting \(F_{X}\) and \(F_{H}\) features into the MLP after fusion. Furthermore, the training process revealed that the time required to complete an epoch using con-feature loss was three minutes shorter than the time needed to train using feature fusion.  
Figure 7 illustrates a visualization of the feature maps after con-feature loss and feature fusion. It can be observed that the visualization of the con-feature loss is concentrated in areas that are more indicative of an expression, while simple feature fusion also focuses on regions that are not related to expressions.  
In addition, we also conducted an in-depth study of the four clipped regions to explore the impact of the important regions on the model's performance. The features \(F_{X}\), \(F_{Lb}\), \(F_{Lt}\), \(F_{Rb}\), \(F_{Rt}\) (depicted in Figure 8) are features that are fused with the fine-grained features \(F_{Swin}\). The results of the experiments are shown in Table 5. 'Max' in the table indicates the selected crucial region.