## 5 Comparision Study  
We use the CoT_AdaptiveViT model to evaluate it in the CK+, RAF-DB, and FERPlus datasets and compare it to state-of-the-art methods. Results on Oulu-CASIA: The performance comparison of the Oulu-CASIA dataset will be presented in two parts. Firstly, for the full experimental data, we used a 10-fold cross-validation method to verify the recognition performance. For the NIR performance comparison, we achieved state-of-the-art performance as shown in Table 4. Unlike previous single convolutional network models, we used a combination of convolutional network and Transformer Encoder to enhance the comprehensive understanding of facial expressions. Second, in the visible light performance comparison, almost all publicly available models are trained based on datasets that are in a strong light environment. Our model, on the other hand, has been experimented under three different lighting conditions and We will show achieved significant performance gains in all of them, the results of the performance comparison with the existing models under strong lighting environments, as shown in Table 5. Compared to HPFS [42], which uses feature separation as a way to overcome the interference of light variations, our method of local relevance learning using the CoT module is more advantageous. Results on RAF-DB: Table 6 shows that our accuracy on the RAF-DB dataset is 91.07%, which outperforms other models. EAC [28] uses randomly removing some facial regions to enhance the model's learning of the non-noise part and thus enhances the model's performance. DAN [47] adopts a multi-task joint approach to capture both local and global feature information. Compared to these methods, our proposed method captures local and global features based on the use of adaptive learning to retain tokens that are most relevant to expression categorization, thus achieving the effect of removing noisy and occluded regions and enhancing computational efficiency. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Year** & **Acc (\%)** \\ \hline LBP-TOP [15] & 2011 & 69.32 \\ DTAGN [16] & 2015 & 66.67 \\ NIRExpNet [17] & 2017 & 78.42 \\ SETFNet [18] & 2019 & 80.34 \\ VGGNet [19] & 2020 & 82.67 \\ Based\_transfer learning [20] & 2022 & 84.83 \\ CoT\_AdaptiveViT(Ours) & 2024 & 89.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison on Oulu-CASIA (NIR dark). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Year** & **Acc (\%)** \\ \hline Spatial-Temporal Network & 2017 & 72.12 \\ HiNet [44] & 2019 & 70.30 \\ VGGNet [19] & 2020 & 84.40 \\ FDRL [45] & 2021 & 88.26 \\ AGFER [46] & 2023 & 89.19 \\ HPFS [42] & 2023 & 87.32 \\ CoT\_AdaptiveViT(Ours) & 2024 & 89.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison on Oulu-CASIA (VL strong). Results on CK+: Table 7 shows that we achieved a high recognition performance on the CK+ dataset by using a 10-fold cross-validation method with 99.20% accuracy. Among them, FER_RN [49] introduces an attention module to redistribute the weight parameters of channel and spatial dimensions, and ZFER [50] performs emotion recognition on faces based on partitions. These methods only take into account local feature information, while our model pairs have a more comprehensive understanding of expression information through the CNN_ViT architecture. Results on FERPlus: Table 8 shows that our model achieves 90.57% accuracy on the FERPlus dataset, demonstrating the excellent recognition performance of our proposed model. VTFF [37] uses ViT and feature fusion to capture global-local information, and FT-CSAT [40] fine-tunes the channel-space attentional transformer to allow the model to be more attentive to expression-related information.