# Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning
## 7 Conclusions  
The proposed research describes the methodology for detecting masked faces by modifying the MobileNetV2 method and the Caffe model. Our research implemented the proposed model with software using Python 3.9 related libraries such as TensorFlow, Pandas, Keras, scikit learn (sklearn) and OpenCV. The installed Anaconda Navigator experience of Juyypter notebook made it easy and convenient to continue our research, compared to using Pytorch. The transfer learning technique helped the model to distinguish between people wearing or not wearing a face mask. The dataset of images and real-time video frames from a webcam were used to validate the efficiency of the face mask classifier. The effectiveness of the model was determined using accuracy, precision, recall, f1-score, and error rate. The proposed methodology worked substantially very well on the dataset with an accuracy of 99.64%. A face mask classifier was also used for real-time video images. Experimental results for video images are accurate when faces are localized by a webcam using the CMNV2 model after the classifier detects the face locations. Using  
Figure 14: Comparison graph of various models’ accuracy.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sl.No.** & **Name of the Model** & **Parameters Used** & **Total Layers Used** \\ \hline
1 & VGG19 & 143,000,000 & 19 \\
2 & VGG16 & 138,000,000 & 16 \\
3 & AlexNet & 62,000,000 & 8 \\
4 & InceptionV2 & 56,000,000 & 48 \\
5 & Inception-ResNetV2 & 56,000,000 & 164 \\
6 & ResNet101 & 44,000,000 & 101 \\
7 & InceptionV4 & 43,000,000 & 164 \\
8 & InceptionV3 & 24,000,000 & 48 \\
9 & Xception & 23,000,000 & 71 \\
10 & ResNet50 & 22,500,000 & 50 \\
11 & MobileNetV1 & 13,000,000 & 30 \\
12 & GoogleNet & 7,000,000 & 27 \\
13 & MobileNetV2 & 3,500,000 & 53 \\
14 & CMNV2 (Proposed Model) & 164,226 & 159 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of various models’ parameters and total layers.  
video surveillance as an input in public places, government agencies can benefit from this detection process. An implementation of this approach was successfully tested in real-time by installing a model of CMNV2. The proposed method can significantly reduce violations through real-time intervention, thus saving time and improving public safety by slowing airborne transmission. A facial mask detection approach in real-time can be used in a variety of places such as in airports, shopping malls, subway stations, temples, offices, hospitals, educational institutions, etc.  
Conceptualization, B.A.K.: methodology, software, writing, editing, original draft preparation, Conceptualization; M.B.: validation, formal analysis, investigation, resources, writing review and editing, visualization, supervision, project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data of written code, all results of photo and real-time video images will be made available on request.  
The authors declare no conflict of interest.