## 4 Discussion
This outcome is strengthened by the Kappa score of 0.49, which falls within the moderate range and demonstrates the model's predictive capability that is not equivalent to chance but rather a moderate agreement between the predicted and true outcome. This demonstrates the model's ability to capture complex facial expressions. Additionally, it provides insights into the model's effectiveness and potential areas for improvement. Table 2 shows the comparison of the proposed approach's results to other methods used during the Aff-Wild2 competition as highlighted in [44]. The proposed approach was able to achieve a higher F1-score compared to other works. This notable performance improvement underscores the efficacy of the method implemented in effectively recognizing and interpreting facial expressions. The margin between the proposed approach and existing methodologies highlights the robustness and potential of the model in using the GCN in emotion recognition. Notably, the model successfully identifies relevant patterns within the data, leading to improved efficacy in emotion recognition. While using a fraction of the Aff-Wild2 dataset, the proposed approach was able to achieve better performance compared to the other works mentioned. In Figure 8, the models explain-ability is displayed, which highlights the areas of strong impact on the class prediction. As observed, the model concentrates on the forehead of the face for the classification of the other class. The focus on the forehead in emotion recognition holds significance for identifying emotions that deviate from the traditional emotional classes. The forehead is a crucial area for detecting subtle and culturally specific emotional cues. Unique expressions may manifest through slight movements or muscle contractions in the forehead, particularly in instances where emotions are complex or socially influenced. Moreover, cultural variations in facial expression lead to differing associations between emotions and facial regions, with some cultures emphasizing forehead movements for emotional expression. Therefore, by focusing on the forehead, the model was able to achieve improved accuracy and comprehensiveness, enabling a better interpretation of emotional cues beyond the constraints of traditional emotional categories. In Figure 9, the model's focus on the forehead, complemented by its focus on the lips, eyes, and cheeks, underscores its proficiency in distinguishing different emotional expressions. By analyzing multiple facial features, including the lips, eyes, and cheeks, the model achieves enhanced precision and depth in deciphering emotional cues. This refined \begin{table}
\begin{tabular}{l c} \hline \hline  & **Method** & **F1-Score** \\ \hline [PERSON] et al. [46] & 0.3075 \\ [PERSON] et al. [47] & 0.3218 \\ [PERSON] [48] & 0.3292 \\ [PERSON] et al. [49] & 0.3337 \\ [PERSON] et al. [50] & 0.3532 \\ Proposed approach & **0.5307** \\ \hline \hline \end{tabular}  
* Value in bold represents the best performance.  
\end{table} 
Table 2: Comparison of the F1-score results from different methods on the Aff-Wild2 dataset. Figure 8: Custom model explain-ability visualization of a face representing the other class. The color bar represents the intensity of the impact of the particular point landmark, with dark blue representing no influence and yellow a strong influence. The radius of the circle also varies according to the intensity of the impact. approach overcomes the constraints of traditional emotional classifications, allowing for a more detailed and comprehensive understanding of emotional expressions. The limitations of this approach include no optimization on the hidden layers, partial use of the dataset, no hyperparameter tuning, and the absence of data normalization. The hidden layers and hyperparameters were not fine-tuned in this study, potentially leaving room for further performance enhancement. The absence of data normalization may have had an impact on the model's ability to generalize well to unseen data.