## 4 Experimental Results
We reported accuracy, precision, recall, and f1-score with respect to each face mask classes for the evaluation of our VGG-16, MobileNetV2, Inception V3, ResNet-50, and CNN models, respectively, as shown in Tables 4-8. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classes** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Face Without Mask (FWOM) & 99.66\% & 0.97 & 0.95 & 0.96 \\ Face with proper mask (FWPM) & 99.62\% & 0.99 & 1.0 & 1.0 \\ Face with improper mask (FWIPM) & 99.67\% & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics using Inception V3 by classes. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Classes** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Face Without Mask (FWOM) & 99.59\% & 0.96 & 0.94 & 0.95 \\ Face with proper mask (FWPM) & 99.4\% & 0.99 & 1.0 & 0.99 \\ Face with improper mask (FWIPM) & 99.47\% & 1.0 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics using ResNet-50 by classes. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Models** & **Accuracy** & **Kapa Values** \\ \hline VGG-16 & 99.8\% & 0.996\% \\ MobileNetV2 & 99.6\% & 0.993\% \\ Inception V3 & 99.4\% & 0.99\% \\ ResNet-50 & 99.2\% & 0.986\% \\ CNN & 99.0\% & 0.983\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative results of models. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classes** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Face Without Mask (FWOM) & 99.45\% & 0.94 & 0.93 & 0.93 \\ Face with proper mask (FWPM) & 99.34\% & 0.99 & 1.0 & 0.99 \\ Face with improper mask (FWIPM) & 99.36\% & 1.0 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistics using CNN by classes. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classes** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Face Without Mask (FWOM) & 99.87\% & 0.99 & 0.98 & 0.98 \\ Face with proper mask (FWPM) & 99.87\% & 1.0 & 1.0 & 1.0 \\ Face with improper mask (FWIPM) & 99.88\% & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics using VGG-16 by classes. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Classes** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Face Without Mask (FWOM) & 99.73\% & 0.98 & 0.96 & 0.97 \\ Face with proper mask (FWPM) & 99.7\% & 1.0 & 1.0 & 1.0 \\ Face with improper mask (FWIPM) & 99.77\% & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics using MobileNetV2 by classes. Further, we also plotted the accuracy, precision, recall, and f1-score of five models with respect to each face mask classes (FWOM, FWPM, and FWIPM) which shown in Figure 17.  
In our proposed system, the VGG-16 model achieved highest accuracy in face mask detection and classification module. The Figure 18, reported the face mask detection and classification in real time. Figure 17: Statistics of five models by classes. The Figure **(a)** show the performance metrics for the identification and classification of face’s mask in three classes FWOM (Face Without Mask), FWPM (Face with Proper Mask), and FWIPM (Face With Improper Mask) by using VGG-16 model. Similar way the **(b–e)** show the performance metrics for the identification and classification of face’s mask in three classes (FWOM, FWPM, and FWIPM) by using the MobileNetV2, Inception V3, ResNet-50 and CNN respectively. Figure 18: VGG-16 face mask detection classification results in real time. Identification and Classification for the Types of Face Masks  
As we mentioned before, in our proposed face mask detection and classification module, the second module is to detect and classify the types of face masks, either it is an N-95 mask or a surgical mask. For this module, we combined the dataset that is described in the Section 3.3.1 by excluding face without mask images. The new combined dataset consists of 28,717 images in total.