## 7 Experimental Setup and Results
### Dataset
As a result, the accuracy of each layer (FC6, FC7, and FC8) was 56.85%, 55.19%, and 56.73%, respectively, as shown in Table 6.  
Further, we applied ML classifiers on FC6, FC7, and FC8 individually to classify the masked dataset. The results show that Subspace Discriminant, Linear SVM, and Quadratic SVM are the best methods for classification in FC6, FC7, and FC8, respectively, in the masked dataset, as shown in Table 7. The accuracy of FC6, FC7, and FC8 was 65.5%, 62.5%, and 61.2%, respectively. Furthermore, the best accuracies of classifiers on each layer of the VGG-16 model are mentioned in the confusion matrix (see Figure 8). \begin{table}
\begin{tabular}{c c} \hline \hline
**VGG-16** & **Accuracy** \\ \hline FC6 & 56.85\% \\ FC6 & 55.19\% \\ FC8 & 56.73\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of VGG-16 pre-trained model on FC6, FC7, and FC8 on the masked dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Classifier Type** & **FC6 Accuracy** & **FC7 Accuracy** & **FC8 Accuracy** \\ \hline Decision Tree & Fine Tree & 53.6\% & 54.6\% & 54.2\% \\  & Medium Tree & 56.7\% & 55.7\% & 56.2\% \\  & Coarse Tree & 56.4\% & 56.2\% & 56.0\% \\ \hline Discriminant Analysis & Linear Discriminant & 49.6\% & 51.4\% & 57.8\% \\ \hline Support Vector Machines (SVM) & Linear SVM & 65.3\% & 62.5\% & 61.2\% \\  & Quadratic SVM & 65.5\% & 64.0\% & 59.8\% \\  & Cubic SVM & 64.4\% & 59.8\% & 57.4\% \\  & Fine Gaussian SVM & 55.8\% & 55.8\% & 55.8\% \\  & Medium Gaussian SVM & 64.2\% & 61.5\% & 60.3\% \\  & Coarse Gaussian SVM & 62.0\% & 59.8\% & 58.4 \\ \hline K Nearest Neighbor (KNN) & Fine KNN & 52.8\% & 51.5\% & 50.9\% \\  & Medium KNN & 58.4\% & 57.2\% & 56.4\% \\  & Coarse KNN & 59.0\% & 58.3\% & 57.8\% \\  & Cosine KNN & 59.3\% & 56.3\% & 55.7\% \\  & Cubic KNN & 58.6\% & 57.2\% & 56.4\% \\  & Weighted KNN & 58.5\% & 57.0\% & 56.2\% \\ \hline Ensemble Classifiers & Boosted Trees & 59.0\% & 58.4\% & 58.6\% \\  & Bagged Trees & 58.7\% & 57.8\% & 57.7\% \\  & Subspace Discriminant & 62.1\% & 58.0\% & 61.0\% \\  & Subspace KNN & 50.4\% & 50.8\% & 51.2\% \\  & RUS Boosted Trees & 48.3\% & 47.6\% & 47.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of ML Algorithm Classification of MLF-W-FER Dataset on VGG-16. \begin{table}
\begin{tabular}{c c} \hline \hline
**VGG-16** & **Classifier** & **Accuracy** \\ \hline FC6 & Subspace Discriminant & 65.5\% \\ FC7 & Linear SVM & 62.5\% \\ FC8 & Quadratic SVM & 61.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Improved accuracy of VGG-16 pre-trained model on FC6, FC7, and FC8 with ML classifiers on the MLF-W-FER dataset. Figure 8 shows that the Linear SVM of FC7 and FC8 was not the optimal classifier to use because it was unable to predict the negative classes. The second-highest accuracies in FC6 and FC7 were selected because they predicted all three classes of the MLF-W dataset. We replaced Linear SVM (62.5%) with Quadratic SVM (62.4%) in FC7 and Linear SVM (61.2%) with Median Gaussian SVM (60.3%) in FC8, respectively, which is shown in Figure 9.  
In the confusion matrix, results showed that negative expressions are detected with low accuracy as compared with positive and neutral expressions on the M-MLFW-FER dataset. It is just because infrequent classes are predicted with low accuracy most of the time. The negative expression in the dataset is the lowest among other datasets, e.g., positive and negative expressions. In this paper, it is assessed how well the SVM classifier and its sub-classes perform on the pre-trained model of VGG-16 features and how applying different classifiers improves the accuracy of the masked dataset on each layer of FC6, FC7, and FC8. Additionally, we have conducted a comparative analysis of our method's accuracy with the results obtained by different authors who employed the VGG-16 and VGG-19 architectures on the MLF-W-FER dataset. Their reported accuracies were 55.6% [46] and 53.54% [47], respectively. By comparing these results, our method has exhibited superior accuracy.