## 4 Discussion  
Table 1 shows a strong bias towards the four classes of neutral, other, happiness, and sadness. To mitigate their impact on the model's learning process and ensure a fair representation of the classes, the weighted loss method described in Section 2.4.2 was implemented. The efficacy of this strategy is evident in Figures 4 and 5, showcasing the model's ability to balance feature representation. It demonstrated good performance across both the underrepresented and well-represented classes. Figure 6: Confusion matrix results of the validation set for all classes. The blue color represents the true positive (TP) predictions. Figure 7: Confusion matrix results of the training set for all classes. The blue color represents the true positive (TP) predictions. Figure 4 shows that the proposed approach was able to achieve good performance on the selected dataset. The results also demonstrated that the model was not overfitting the training data, since the difference in the measures of the validation and training sets had a margin of less than 5%. The model's ability to achieve strong performance on the underrepresented classes was expected, as there were limited data to learn from, coupled with the adopted weighted loss strategy. The happiness class's weak performance was due to it being mistaken as neutral with an error of 14.32%, as revealed by the confusion matrix. The other and neutral classes were also often confused, leading to their low performance scores. This phenomenon can be attributed to the minute differences in the features and linkages obtained from the facial mesh that make it difficult to distinguish between these classes. These outcomes also suggest emotion recognition via image alone might combine these emotions for simplicity and improved performance, or, in contrast, require extra inputs for full classification, such as voice stress analysis, to better segregate these otherwise less distinguishable emotions. While the model's overall performance did not reach a high standard, achieving a mean TP accuracy of 58.76% is notable. This outcome is particularly significant given the complexity of the dataset, which encompasses a wide range of facial expressions. The dataset, collected \"in-the-wild\", mirrors real-world scenarios as closely as possible. In such settings, distinguishing between facial expressions becomes inherently challenging, as there are no standardized poses or specific facial reactions to replicate. Instead, the expressions captured reflect visceral reactions and perceptions, making accurate predictions more complex. The difficulties encountered in distinguishing emotional classes also highlight the intricate nature of both data annotation and facial expression decoding. The results of the F1-score reveal that the proposed approach has some hurdles to overcome. The approach's limitations were noticed in the fear class, where the model's ability to correctly identify instances belonging to the positive class and to distinguish TP predictions from FP was notably lacking. Such a score suggests significant challenges in accurately classifying the fear class and highlights potential limitations in the model's ability to generalize to unseen data. This outcome was expected as the fear class was the lowest representation, and although the weighted loss was adopted for performance boost and imbalance stabilization, it was not able to improve on the generalizability of unseen data due to the weak representation of this class. Achieving a 53.07% F1-score, while not considered high, is still acceptable and reasonable in this type of application [44; 46; 47; 48; 49; 50]. The complexity and inherent ambiguity of emotion recognition, coupled with the dataset's representation, make it challenging to achieve notable performance. However, the proposed approach was able to demonstrate the ability to capture the key patterns from within the data and thereby make informed predictions within the given constraints.