## 6 Discussion  
This section of our paper highlights key notations and compares our models with existing works in the field. We delve into specific notations crucial for understanding our approach and provide a thorough comparison of our models with those previously established in the literature. The comparison between models with all layers frozen with no histogram and those with fine-tuned models with no histogram reveals significant differences in the performances across datasets. For instance, on the KDEF dataset, the VGG19 with all layers frozen and no histogram achieved an accuracy of 54.76%, whereas the fine-tuned VGG19 with no Histogram achieved nearly 94.22%. This variation underscores the limitations of freezing all layers, as it limits the model's adaptability to the new task/domain by preserving fixed feature extraction mechanisms. Oppositely, unfreezing the last five layers allows for fine-tuning, enabling the model to learn task-specific representations and enhance the performance using pre-trained weights while accommodating adjustments to suit the new task requirements. Furthermore, we proceed to compare the models' performance post-histogram equalization. Notably, all hyper-tuned models exhibited slightly improved performances with histogram equalization. As indicated in Table 5, hyper-tuned models using histogram-equalized images showed a 0.3-2% enhancement on average compared to their counterparts without histogram equalization. However, exceptions were observed on the CK+ dataset, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Type** & **Accuracy** \\ \hline [PERSON] et al. [65] & Efficient-SwishNet & 63.4\% \\ [PERSON] et al. [66] & Xception & 65.97\% \\ [PERSON] et al. [67] & CNN + Attention & 70.02\% \\ [PERSON] et al. [72] & Pre-trained VGG19 & 66.6\% \\ [PERSON] et al. [22] & 4 Ensemble Model & 75.06\% \\ Proposed Model & Histogram + Pre-trained VGG16 & **69.65\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of methods on FER2013 dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Type** & **Accuracy** \\ \hline [PERSON] et al. [63] & 2B (N + M) Softmax & 97.1\% \\ [PERSON] et al. [65] & Efficient-SwishNet & 100\% \\ [PERSON] et al. [67] & CNN + Attention & 98\% \\ [PERSON] et al. [72] & Pre-trained VGG19 & 98.98\% \\ Proposed Model & Histogram + Pre-trained VGG16 & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of methods on CK+ dataset. where both hyper-tuned models, with and without histogram equalization, exhibited similar performances. In the case of the Filtered FER2013 dataset, one interesting observation worth discussing is the performance on the FER2013 dataset with the image size set to 144 \(\times\) 144. Despite the original VGG16 and VGG19 models being trained on the 224 \(\times\) 224 \(\times\) 3 ImageNet dataset, the 144 \(\times\) 144 image size showed promising results across all experiments. This deviation from the conventional image size might be attributed to the potential loss of information and degradation of image quality when resizing an image to larger dimensions. When resizing an image to a larger size, the existing pixels are stretched to fill the new dimensions, which can lead to blurriness or pixelation. This loss of information may have been mitigated by selecting an optimum size of 144 \(\times\) 144, which preserved the originality of the image while maintaining a balance between image quality and resolution. Moreover, when we applied cosine annealing to the fine-tuned VGG16 models with histograms, the model's accuracy initially plateaued at around 67.57% on the FER2013 dataset. However, after reloading the model and running the model for an additional 30 epochs with cosine annealing, the accuracy improved to 69.65%. But which specific features of the cosine annealing rate differ from those in normal learning cycles, resulting in enhancements in model performance should be interesting to find.