## 4 Experiments
### Ablation Studies
#### 4.4.2 Visualization and Analysis  
Figure 8 shows the results of the visualization of the confusion matrix of the CoT_AdaptiveViT model on the Oulu-CASIA (NIR, VL), CK+, RAF-DB, and FERPlus datasets. From the overall viewpoint, the model is highly discriminative on the categories of Happy, and Surprised and performs well on all other categories. From the recognition results of each category, our proposed model has an excellent global perception ability to adapt to complex scenes. Face expression images under different lighting conditions not only have changes in feature expressions brought about by lighting changes, but are often accompanied by low-resolution images with a lot of noise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**CoT**} & \multirow{2}{*}{
\begin{tabular}{c} **Adaptive** \\ **Method** \\ \end{tabular} } & \multicolumn{3}{c}{**Visible Light**} & \multicolumn{2}{c}{**Near-Infrared**} \\ \cline{3-6}  & & **Strong** & **Weak** & **Dark** & **Dark** \\ \hline \(\times\) & \(\times\) & 86.16 & 82.10 & 79.73 & 86.09 \\ \(\surd\) & \(\times\) & 87.50 & 84.25 & 80.77 & 86.30 \\ \(\times\) & \(\surd\) & 88.65 & 82.78 & 80.02 & 87.09 \\ \(\surd\) & \(\surd\) & 89.47 & 84.76 & 82.28 & 87.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study on the Oulu-CASIA dataset. The base model refers to the model without the incorporation of the CoT module and the adaptive method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CoT**} & \multirow{2}{*}{
\begin{tabular}{c} **Adaptive** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{**CK+**} & \multirow{2}{*}{**RAF-DB**} & \multirow{2}{*}{**FERPlus**} \\ \hline \(\times\) & \(\times\) & 97.97 & 89.7 & 89.76 \\ \(\surd\) & \(\times\) & 98.98 & 90.61 & 90.45 \\ \(\times\) & \(\surd\) & 98.71 & 90.78 & 90.32 \\ \(\surd\) & \(\surd\) & 99.20 & 91.07 & 90.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study on the CK+, RAF_DB, and FERPlus datasets. The base model refers to the model without the incorporation of the CoT module and the adaptive method.  
To deeply explore the CoT module's ability to learn the local relevance of face expression features in different environments, we visualized the feature maps before and after the CoT module in the inference stage, as shown in Figure 9. Randomly selected images of face expressions in different environments and with different poses were processed by the CoT module, and we observed that the model focuses more on specific regions of attention, which demonstrates the role of the CoT module in strengthening the model's ability to recognize important features. At the same time, we notice that the local correlations between different regions are enhanced, which helps the model to better capture the subtle changes and emotional information in facial expressions and, mitigate the effects of lighting variations and noise on expression features.  
Figure 8: Confusion matrix of CoT_AdaptiveViT on Oulu-CASIA, CK+, RAF-DB, FERPlus. (**a**) Oulu_VL_strong; (**b**) Oulu_VL_weak; (**c**) Oulu_VL_dark; (**d**) Oulu_NIR_dark; (**e**) CK+; (**f**) RAF-DB; (**g**) FERPlus.  
Figure 9: From **top** to **bottom** shows the input image, the feature map before the CoT module, and the feature map after the CoT module.