# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Masked Face Recognition Approaches
#### 5.3.3 Deep Learning-Based Masked Face Approaches  
This subsection centers on leveraging deep learning techniques, often employing attention mechanisms to prioritize unmasked regions for feature extraction while attempting to mitigate the impact of the mask itself. Some authors opt to train models using a combined dataset of masked and unmasked faces, facilitating robustness to varying facial conditions. Unlike the previous methods, this approach does not require additional preprocessing steps such as face restoration or cropping of the upper face region. Table 8 presents a summary of the methodologies employed within this paradigm. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Year** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **EER\%** \\ \hline YOLOv3 [102] & & & 93\% & - & - & - & - \\ YOLO-face + VGGFace2 & 2020 & Custom dataset & 96.8\% & - & - & - & - \\ RetinaFace + VGGFace2 & & & 94.5\% & - & - & - & - \\ \hline \multirow{4}{*}{MFCosface [45]} & \multirow{4}{*}{2021} & LFW\_m & 99.33\% & - & - & - & - \\ \cline{2-6}  & & CF\_m & 97.03\% & - & - & - & - \\ \cline{2-6}  & & MFR2 & 98.50\% & - & - & - & - \\ \cline{2-6}  & & RMFRD & 92.15\% & - & - & - & - \\ \hline \multirow{4}{*}{MFR-CDC [103]} & \multirow{4}{*}{2021} & SMFRD & 95.31\% & - & - & - & - \\ \cline{2-6}  & & RMFRD & 95.22\% & - & - & - & - \\ \hline RggNet [104] & 2021 & Custom dataset & 60.8\% & 77.7\% & 51.9\% & - & - \\ \cline{2-6}  & & LFW\_m & 99.33\% & - & - & - & - \\ \cline{2-6}  & & CF\_m & 97.03\% & - & - & - & - \\ \cline{2-6}  & & MF2 & 98.50\% & - & - & - & - \\ \cline{2-6}  & & RMFRD & 92.15\% & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of deep learning-based approaches for masked face recognition. Building upon the ArcFace architecture, [PERSON] et al. [105] introduced an end-to-end approach for training face recognition models, incorporating modifications to the backbone and loss computation processes. Additionally, they implemented data augmentation techniques to generate masked versions of the original dataset and dynamically combine them during training. By integrating the face recognition loss with the mask-usage loss, they devised a novel function termed Multi-Task ArcFace (MTArcFace). Experimental results demonstrated that their model served as the baseline when utilizing masked faces, achieving a mean accuracy of 99.78% in mask-usage classification, while maintaining comparable performance metrics on the original dataset. On a parallel front, [PERSON] et al. [45] proposed their masked face recognition algorithm, leveraging large-margin cosine loss (MFCosface) to map feature samples in a space with a reduced intra-class distance and expanded inter-class distance. They further developed a masked face image generation algorithm based on the detection of key facial features, enabling the creation of corresponding masked face images. To enhance their model's performance and prioritize unmasked regions, they introduced an Att-inception module combining the Inception-Resnet module and the convolutional block attention module. This integration heightened the significance of unoccluded areas in the feature map, amplifying