## 4 Review of Mask-Face Detection Methodologies
### Three-Class Mask-Face Detection Methodologies
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref. \& \& Publish Date** & **Datasets** & **Models** & **Detection Performance** & **Merits** & **Limitations** \\ \hline May 2021, [62] & A combination of their own photos, & The combination of an improved CSPMarkNetS3, the adaptive image scaling & mAP of 98.3\%, when IOU & The developed model had a high accuracy and high & This study performed \\  & MaskedFace-Net & & Improvendor CSPMarkNetS3, the adaptive image scaling & was 0.5. & high accuracy and high & mask-face detection without \\  & MaskedFace-Net & & algorithm and the improved & frame rate. The customized & the consideration of dim \\  & datasets & PANectra structure based on & dataset induced various & light conditions. & \\  & & YOLOv4 [41] & & and various situations of & \\ \hline August 2021, [61] & A customized dataset of 7892 images & Face detection algorithms in OpenCV \& ResNet50 [28] & Accuracy of 92\% & Make a comparison between light-weighted network and deep structure network. & Neglected the situations of mask-face detection in video surveillance. \\  & & and NASNet/Mobile [64] & & the numbers of samples of & \\  & & and classification & & each class in the dataset are & \\  & & & & about the same. & \\ \hline October 2021, [65] & A customized dataset of 3300 images and four videos & Faster R-CNN [14] and Inception V2 [63] & Accuracy of 99.8\% & The algorithm successfully & Compared with the other \\  & & & Inception V2 [63] & & identified people who did & two types of results, the accuracy of incorrect \\  & & & & distance and were too near & mask-wearing class was \\  & & & & each another. It also took & not satisfactory. \\ \hline  & March 2021, [66] & A customized dataset of 52,635 images & Tweaked and modified feature extraction networks for the VOLOv1 [15], tiny YOLOv2 [5], tiny YOLOv3 [5] and tiny YOLOv4 [41] & mAP of 70.25\% for M-T & The proposed dataset & There were large gaps & between the accuracy of the \\  & & & for my YOLOv1 [15], tiny YOLOv2 [5], tiny YOLOv3 [35] and tiny YOLOv4 [42] & & mask area, which enhanced & three classes of detection, especially for incorrect \\ \cline{2-6} _Others_ & May 2021, [67] & A combination of 14,409 images from & YolovvFaceMask & mAP of 88.82\% & The research considered the detection of different types of face masks in different & The detection accuracy was low. The number of samples in each class in training & low. The number of samples in each class in training & in each class in training \\  & & and RMFD & & & of different resolutions. & dataset was not specified. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref. \& Publish Date** & **Datasets** & **Models** & **Detection Performance** & **Merits** & **Limitations** \\ \hline August 2021, [68] & A customized dataset of 3200 images & CNN Models of MobileNetV2 [32], Xception [11], DeepNet50 [2], DeepNet50 [33], DeepNet50 [34], DeepNet50 [35], DeepNet50 [36], DeepNet50 [37], DeepNet50 [38], DeepNet50 [39], DeepNet50 [40], DeepNet50 [41] with & F1-score of 85\% & In the dataset, the class of incorrect mask-wearing was subdivided into ten more specific types. \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline October 2021, [69] & A customized dataset of 11,000 images & Tiny YOLO v4-SPP & mAP of 64.31\% & In addition to the detection of the classes of & The detection accuracy was lower than other \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline December 2021, [70] & ISI-UVHD and & RetinaFace as the detector. & Accuracy of 98.20\% & The authors not only & Compared with the other \\  & ISI-UVHD (proposed & ResNet50 [71], & considered mask-face & two categories, the class of \\  & for the first time) & Inception-v3 [38], & detection, but also the & incorrect mask-wearing was \\  & & & & & detection of face-hand \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\ \end{tabular}
\end{table}