# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Mask Recognition Approaches
#### 5.1.3 Single Shot Detector
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Year** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Fine-Tuning of InceptionV3 [64] & 2020 & SMFD & 100\% & 100\% & - & - \\ \hline MobileNetV2 + SVM [65] & 2020 & Private Dataset & 97.11\% & 95.08\% & 94.84\% & - \\ \hline \multirow{4}{*}{Resnet50 + SVM + ensemble algorithm [66]} & \multirow{2}{*}{2021} & RMFRD & 99.64\% & - & - & - \\ \cline{2-5}  & & RMFRD & 99.64\% & - & - & - \\ \cline{2-5}  & & KFW & 100\% & - & - & - \\ \hline \multirow{2}{*}{Faster\_RCNN + InceptionV2 + BLS [31]} & \multirow{2}{*}{2021} & WMD Simple Scene & - & 96.46\% & 98.20\% & 97.32\% \\ \cline{2-5}  & & WMD Complex Scene & - & 94.22\% & 88.24\% & 91.13\% \\ \hline \multirow{4}{*}{Max pooling [28]} & \multirow{4}{*}{2021} & \multirow{4}{*}{RMFRD + SMFD + Own Dataset} & 98.67\% & - & - & - \\ \cline{2-5}  & Average pooling & & 96.23\% & - & - & - \\ \cline{2-5} MobileNetV2 & & & 99.82\% & - & - & - \\ \hline CNN [60] & 2021 & Kaggle Dataset & 98\% & 98\% & 97\% & 98\% \\ \hline \multirow{2}{*}{ResNet50 + bounding box transformation [61]} & \multirow{2}{*}{2021} & MAFA dataset Face Detection & - & 99.2\% & 99\% & - \\ \cline{2-5}  & & MAFA dataset Mask Detection & - & 98.92\% & 98.24\% & - \\ \hline RetinaFace + CNN(NASNetMobile) [62] & \multirow{2}{*}{2021} & RMFRD + Lareel (Kaggle) & 99.23\% & 98.28\% & 100\% & 99.13\% \\ \cline{2-5}  & RetinaFace + CNN(Dense Net121)) & & 99.49\% & 99.70\% & 99.12\% & 99.40\% \\ \hline SSDMV2 [29] & 2021 & Self-made Dataset of Masked Faces & 92.64\% & - & - & 93\% \\ \hline Fusion Transfer Learning [68] & 2022 & RMFRD and MAFA & 97.84\% & - & 97.87\% & 98.13\% \\ \hline \multirow{4}{*}{Customized CNN + Image Preprocessing Techniques [63]} & \multirow{2}{*}{2023} & RILFD & 97.25\% & 96.20\% & 97.34\% & 96.77\% \\ \cline{2-5}  & & MAFA & 95.74 & - & 94.29\% & - \\ \cline{2-5}  & & MOXA & 94.37\% & - & 95.28\% & - \\ \cline{2-5}  & & RMFRD & 99.63\% & - & 99.69\% & - \\ \hline SSD, ResNet-50, and DeepSiamese Neural Network [69] & 2023 & RMFRD + Lareel & 98.24\% & - & - & - \\ \hline MobileNetV2 and Caffe-based SSD [70] & 2023 & Efficient Face Mask Dataset & 97.81\% & - & - & 98\% \\ \hline CMNV2 [71] & 2023 & The Prajna Boundary & 99.64\% & 100\% & 99.28\% & 99.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of different face mask recognition models. time with high speed and accuracy. [PERSON] et al. [69] proposes an autonomous system for real-time face mask recognition during the COVID-19 pandemic. Leveraging a pre-trained ResNet-50 model, the system fine-tunes a new classification layer to distinguish masked from non-masked individuals. Using adaptive optimization techniques, data augmentation, and dropout regularization, the system achieves high accuracy. It employs a Caffe face detector based on SSD to identify face regions in video frames. Faces without masks undergo further analysis using a deep Siamese neural network (based on VGG-Face) for identity retrieval. The classifier and identity model achieve impressive accuracies of 99.74% and 98.24%, respectively. [PERSON] et al. [70] presents the Rapid Real-Time Face Mask Detection System (RRFMDS) which is an automated method designed to monitor face mask compliance using video surveillance. It utilizes a Single-Shot Multi-Box Detector for face detection and a fine-tuned MobileNetV2 for classifying faces as masked, unmasked, or incorrectly masked. Seamlessly integrating with existing CCTV infrastructure, the RRFMDS is efficient and resource-light, ideal for real-time applications. Trained on a custom dataset of 14,535 images, it achieves high accuracy (99.15% on training and 97.81% on testing) while processing frames in just over 0.14 s. [PERSON] et al. [67] developed a resource-efficient face mask recognition model using a combination of deep learning technologies including TensorFlow, Keras, and OpenCV.