## 5 Results
### Ck+  
For the CK+ dataset, our models achieved the highest accuracies among the various architectures. Specifically, the fine-tuned VGG16 and VGG19 models, both with and without histogram equalization, demonstrated exceptional performances with accuracies of 100% and 98.99%, respectively (Table 5). Additionally, these models exhibited strong performances in terms of weighted F1, AUC-ROC, and AUC-PRC metrics. The VGG16 and VGG19 models, in which the layers of the base models were frozen and not fine-tuned, exhibited lower performances across all evaluation metrics, achieving 96.97% and 90.91%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy (\%)** & **Weighted-F1 (\%)** & **AUC-ROC (\%)** & **AUC-PRC (\%)** \\ \hline VGG19 with all layer freezed + No Histogram & CK+ & 90.91 & 91.00 & 99.00 & 96.00 \\ \hline Fine-tuned VGG19 + No Histogram & CK+ & 97.98 & 98.00 & 100.00 & 99.00 \\ \hline Fine-tuned VGG19 + Histogram & CK+ & 98.99 & 99.00 & 100.00 & 100.00 \\ \hline VGG16 with all layer freezed + No Histogram & CK+ & 96.97 & 97.00 & 100.00 & 99.00 \\ \hline Fine-tuned VGG16 + No Histogram & CK+ & 97.98 & 98.00 & 100.00 & 100.00 \\ \hline Fine-tuned VGG16 + Histogram & CK+ & **100** & 100.00 & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different models on various datasets.  
To gain insights into the inner workings of our CNN models, we employed Grad-CAM (Gradient-weighted Class Activation Mapping) [79]. Grad-CAM is a visualization technique that highlights important regions of an input image used by a CNN for classification. By examining the Grad-CAM visualizations (Figure 3), we observed that our models were effectively focusing on crucial areas (red marked areas) where important features are located. This suggests that our models are making informed decisions based on relevant image features.  
Furthermore, the superiority of our models can be visualized when we examine its confusion matrix. Its confusion matrix (Figure 4) reveals a zero misclassification rate on the CK+ datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** \\ \hline Angry & 0.95 & 0.93 & 0.94 \\ Disgust & 0.94 & 0.94 & 0.94 \\ Fear & 1.00 & 0.90 & 0.95 \\ Happy & 1.00 & 1.00 & 1.00 \\ Neutral & 0.95 & 0.95 & 0.95 \\ Sad & 0.95 & 1.00 & 0.97 \\ Surprise & 0.93 & 1.00 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of fine-tuned VGG19 on different classes on the KDEF dataset.  
Figure 3: GradCam from CK+ Dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy (\%)** & **Weighted-F1 (\%)** & **AUC-ROC (\%)** & **AUC-PRC (\%)** \\ \hline VGG19 with all layer freezed + No Histogram & KDEF & 54.76 & 53.93 & 86.65 & 59.29 \\ \hline Fine-tuned VGG19 + No Histogram & KDEF & 94.22 & 94.20 & 99.64 & 98.25 \\ \hline Fine-tuned VGG19 + Histogram & KDEF & **95.92** & 95.90 & 99.60 & 98.53 \\ \hline VGG16 with all layer freezed and + No Histogram & KDEF & 56.80 & 56.62 & 88.97 & 62.80 \\ \hline Fine-tuned VGG16 + No Histogram & KDEF & 92.18 & 92.12 & 99.62 & 98.08 \\ \hline Fine-tuned VGG16 + Histogram & KDEF & 92.86 & 92.87 & 99.69 & 98.34 \\ \hline VGG19 with all layer freezed + No Histogram & FER2013 & 35.99 & 29 & 57.70 & 19.73 \\ \hline Fine-tuned VGG19 + No Histogram & FER2013 & 69.06 & 68.57 & 80.87 & 52.26 \\ \hline Fine-tuned VGG19 + Histogram & FER2013 & 69.44 & 68.34 & 80.20 & 51.58 \\ \hline VGG16 with all + freezed with No Histogram & FER2013 & 41.20 & 35.53 & 60.36 & 22.22 \\ \hline Fine-tuned VGG16 + No Histogram & FER2013 & 68.8 & 69.29 & 81.34 & 52.37 \\ \hline Fine-tuned VGG16 + Histogram & FER2013 & **69.65** & 69.65 & 80.75 & 51.83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._