# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
## 6 Results Visualization  
All of the research was completed on a laptop with an Intel(R) with Core(TM) of i3-1005G1 CPU processor with 1.2 GHz, 8 GB of RAM, and a 64-bit operating system with an x64-established processor. For the construction and implementation of the many experimental paths in the proposed paper, Python 3.9 version kernel, and Jupyter Notebook tools, were utilized. The proposed CMNV2 model was applied to the photo image dataset and to the extracted real-time video images from the webcam for the classifications. The proposed model makes predictions based on the training dataset's pattern and labels. The visualization of the classification results of the photo images data and video images are shown in Figure 10 and Figure 11, respectively. The face with a proper mask is displayed with a green bounding box and the face without/with an incorrect mask is displayed with a red bounding box. The classification accuracy of our model is 99.64% on the test dataset. The accuracy and the loss curve for the training and testing data of the images are shown in Figure 12. The training and test accuracy are represented by the green colour and blue colour, respectively, in Figure 12a. The training and testing loss are represented by the green colour and blue colour, respectively, in Figure 12b. Table 3 indicates that the proposed model produces better results in all metrics for the classification of faces with and without masks. The proposed CMNV2 model was mainly tested and trained on images of straight faces with and without masks. This model performs less accurately in real-time video images with sudden facial movements than in still images. Figure 11: Face detection with and without masks in real-time video images. Figure 10: Face detection with and without masks in photo images. Our CMNV2 model in terms of accuracy is represented in Table 4 and displayed in Figure 14. Our proposed model of CMNV2 requires fewer parameters for face mask detection compared to other different methodologies [50; 51] as illustrated in Table 5.  
The \(224\times 224\) photo image dimension that was utilized in this research presents higher classification accuracy as compared to \(192\times 192\), \(160\times 160\), and \(128\times 128\) size image dimensions. This has been theoretically and practically seen throughout testing on the photo and real-time video images. This proposed method can be integrated with CCTV cameras, and the data can be used to determine whether any of their employees or patients are not wearing masks in hospitals or offices. If a person is discovered without a mask, a message can be sent to instruct them to wear one. This methodology can be helpful to maintain safety requirements in an attempt to prevent the spread of any airborne disease. Limitations of the study: The proposed CMNV2 model was mainly tested and trained on images of straight faces with and without masks. Our model is less accurate with sudden facial movements in real-time video images, than it is with still faces. The overall model accuracy of our research decreased due to this. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**SI.No.** & **Model Name** & **Year** & **Accuracy\%** \\ \hline
1 & ResNet50 [19] & 2021 & 47.00\% \\
2 & OpenFace [16] & 2020 & 63.18\% \\
3 & DeepFace [16] & 2020 & 63.78\% \\
4 & MTCNN+FaceNet [48] & 2020 & 64.23\% \\
5 & FaceNet [16] & 2020 & 67.48\% \\
6 & VGG-Face [16] & 2020 & 68.17\% \\
7 & IAMGAN [28] & 2020 & 86.50\% \\
8 & FaceMaskNet2I [29] & 2022 & 88.92\% \\
9 & DSA-Face [31] & 2021 & 91.20\% \\
10 & CNNs [20] & 2022 & 91.30\% \\
11 & CBAM [30] & 2021 & 92.61\% \\
12 & SSDMNV2 [32] & 2021 & 92.64\% \\
13 & GANs [33] & 2020 & 94.10\% \\
14 & MGL [34] & 2020 & 95.00\% \\
15 & FaceNet [49] & 2020 & 97.00\% \\
16 & LPP [35] & 2020 & 97.94\% \\
17 & CNN [36] & 2022 & 98.00\% \\
18 & ResNet50 [37] & 2021 & 98.20\% \\
19 & LW-CNN [50] & 2022 & 98.47\% \\
20 & CMNV2 (Proposed Model) & 2022 & 99.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of various models’ accuracy.