## 4 Review of Mask-Face Detection Methodologies
### Two-Class Mask-Face Detection Methodologies
\begin{table}
\begin{tabular}{c|c|p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline  & **Ref. \& Publish Date** & **Datasets** & **Models** & **Detection Performance** & **Metrics** & **Limitations** \\ \hline \multirow{10}{*}{_Traditional learning_} & October 2020, [25] & A combination of CASPEAL dataset, AISL dataset, INRIA dataset and RMFD dataset & Haar Wavelet, Local Binary Pattern (LRP) and Histogram of Oriented Gradient (HOC) dataset & Accuracy of 86.9\% & The work focused on masks multi-pose faces using traditional methods. & The detection accuracy was lower than that of the neural network, and self-made feature pictures \\  & January 2021, [26] & A customized dataset of 1292 images & FMRN (A customized CNN) of 1292 images & Day time accuracy of 95.8\%, Night time accuracy of 94.6\% & The authors added the Night time accuracy of 94.6\% & The authors added the human skeleton analysis part to analyze the human posture, and the influence light on the detection system was considered. \\ \hline \multirow{10}{*}{_Two-stage_} & July 2021, [27] & A customized mask-face dataset and video surveillance & ResNet50 [28] & Accuracy of 98.2\% & Besides mask-face detection, the identity of persons without a mask was confirmed. It required less memory and has low inference time. \\  & March 2021, [31] & A customized dataset of 4095 images & A transfer learning on MobileNetV2 [32] and use SSD [33] detector. & Accuracy of 98\% & Performed high detection accuracy and could work on devices with limited computational resources. \\ \cline{2-6}  & June 2021, [34] & The customized dataset of 350 MB size & Mask Detection Model (MDM) with YOLOv3 [35] detector & Average confidence level are 93\% (A), 96\% (B) and 93\% (C) and 93\% (D) and videos with the group of people (type C). & The study lacked the individual images (type A), group people (type B), mask-face detection models. \\ \cline{2-6}  & December 2021, [36] & A combination of 1916 masked and 1930 unmasked images & Stacked ResNet-50 [28], YOLOv5 and density-based spatial clustering of applications with noise (DBSCAN) & mAP of 98\% & In addition to detecting masks, social distancing norms were also detected, and real-time detection was performed. \\ \cline{2-6}  & \multicolumn{1}{p{142.3 pt}}{} & \multicolumn{1}{p{142.3 pt}}{} & \multicolumn{1}{p{142.3 pt}}{} & \multicolumn{1}{p{142.3 pt}}{} & \multicolumn{1}{p{142.3 pt}}{} \\ \end{tabular}
\end{table} 
Table 2: Summary of 2-class mask-face detection methodologies. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref. \& \& **Fubini Date** & **Datasets** & **Models** & **Detection Performance** & **Merits** & **Limitations** \\ \hline January 2022, [37] & A customized dataset of 9000 images & YOLOv5 & Accuracy of 92\% & The authors combined & The dataset did not contain attention mechanism with & pictures of multiple people \\  & & of 9000 images & & & YOLOv5, thereby improving & for mask-face detection. \\  & & & & the accuracy of mask & wearing detection in dim \\  & & & & light conditions. & \\ \hline February 2022, [88] & A customized dataset & A customized CNN model with & Accuracy of 98\% & Compared with other & This model did not consider \\  & & ResNet+10 [2]architecture & & the accuracy rate was higher & \begin{tabular}{} \end{tabular}  \\ \hline February 2022, [89] & WiderFace and & A customized frame & Without mask: precision of & They conducted ablation & Surveillance video was not \\  & MAFA & \begin{tabular}{} \end{tabular}  & 99.3\%, recall of 96.2\%, & studies using different & included in the dataset, \\  & & \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  & and real-time detection was \\  & & \begin{tabular}{} \end{tabular}  & \ \begin{tabular}{} \end{tabular}  & and the size of proposed & not considered.