## 4 Experimental Results  
This study deals with controlling the spread of COVID-19 according to the WHO's given instructions for public safety and awareness. As we discussed in the methodology section, our proposed model consists of two modules. The first module deals with contact-free temperature detection and abnormal temperature individuals' record-keeping system to disinfects using sanitizing liquid. In the second module of our proposed system, we implemented a mask detection and classification system in which our system is trained to detect and classify the individuals' face mask in three classes, which are face with proper  
Figure 12: CNN work flow to detect faces with mask and without mask. mask (FWPM), face with improper mask (FWIPM), and face without mask (FWOM) while they are passing from SSDWG. The system is trained using different machine learning models, and we achieved good accuracy on the test data in experiments. Our proposed SSDWG is designed for all kinds of public places, we want to ensure that individuals have to pass from SSDWG. The attached sensors to this gate are activated; a contact-free temperature measuring sensor detects the person's temperature. The CCTV module captures the image from a surveillance video of the person whose temperature 100F or more than 100F, and the system will store the data in the database with the status of suspected. As mentioned before, the face mask detection module detects and classifies individuals' face mask in three classes: FWPM, FWIPM, and FWOM. The disinfection module showers sanitizing liquid on the person for 10 s while passing from SSDWG. The person controlling the entrance can divert suspicious persons for COVID-19 testing. For the experiments, we used three datasets, as we mentioned above in dataset Section. In our proposed model, we achieved a high accuracy on the Github (Masked Face-Net). We fine-tuned five pre-trained models and re-trained these models to perform testing using test dataset. Our proposed model achieved a high accuracy on the combined dataset using the VGG-16. Figure 13 shows the comparative accuracy of VGG-16 on all datasets (MAFA and Masked Face-Net are available on Github, and third dataset from Bing). By using the VGG-16, MobileNetV2, Inception V3, ResNet-50, and CNN models, we achieved 99.8%, 99.6%, 99.4%, 99.2%, and 99.0% accuracies, respectively, in the face mask detection and classification module of our proposed SSDWG system. We plotted the confusion matrix to depict our five model's performances on test data. The VGG-16 and MobileNet V2 achieved the highest accuracy. Figures 14-16 show the confusion matrix of these five fine-tuned models. Figure 13: Comparative accuracy of VGG-16 on different datasets. Figure 16: The confusion matrix for the identification and classification of face mask in three classes FWOM (Face Without Mask), FWPM (Face with Proper Mask), and FWIPM (Face With Improper Mask) by using VGG-16 model. Figure 14: The confusion matrixes of two models: (**a,b**) show the confusion matrix for the identification and classification of face mask in three classes FWOM (Face Without Mask), FWPM (Face with Proper Mask), and FWIPM (Face With Improper Mask) by using the CNN and ResNet-50 models, respectively. Figure 15: The confusion matrixes of two models: (**a,b**) show the confusion matrix for the identification and classification of face mask in three classes FWOM (Face Without Mask), FWPM (Face with Proper Mask), and FWIPM (Face With Improper Mask) by using Inception V3, and MobileNetV2 models respectively. We also compared the results of these five models in terms of their accuracy and kappa values that are shown in Table 3.  
We computed and compared the accuracy, precision, and other parameters with respect to each face mask class (FWOM, FWPM, and FWIPM) for the evaluation of our models individually.