# 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
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Year** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **EER\%** \\ \hline \multirow{4}{*}{MTArcFace [105]} & \multirow{4}{*}{2022} & Masked-LFW & 98.92\% & - & - & - & - \\ \cline{3-8}  & & Masked-CF\_FF & 98.33\% & - & - & - & - \\ \cline{3-8}  & & Masked-CF\_FF & 88.43\% & - & - & - & - \\ \cline{3-8}  & & Masked-AGEDB\_30 & 93.17\% & - & - & - & - \\ \cline{3-8}  & & MFR2 & 99.41\% & - & - & - & - \\ \hline Deepmasknet [33] & 2022 & MDMFR & 93.33\% & 93.00\% & 94.50\% & 93.74\% & - \\ \hline \multirow{2}{*}{MFR-CNNandLBP [32]} & \multirow{2}{*}{2022} & COMASK20 & - & 87\% & 87\% & 87\% & - \\ \cline{3-8}  & & Essex dataset & - & 99\% & 97\% & 98\% & - \\ \hline \multirow{4}{*}{MFR-DML and FaceMaskNet-21 [106]} & \multirow{4}{*}{2022} & User dataset & 88.92\% & - & - & - & - \\ \cline{3-8}  & & & RMFRD & 82.22\% & - & - & - & - \\ \cline{3-8}  & & User dataset & 88.186\% & - & - & - & - \\ \hline \multirow{4}{*}{Att-Based-MFR (CASIA-Webface\_m) [48]} & \multirow{4}{*}{2022} & LFW\_m & 99.43\% & 99.30\% & 99.56\% & 99.43\% & - \\ \cline{3-8}  & & AgeDB-30\_m & 95.86\% & 93.83\% & 97.82\% & 95.78\% & - \\ \cline{3-8}  & & CFF-F\_m & 97.47\% & 96.77\% & 98.69\% & 97.27\% & - \\ \cline{3-8}  & & MFR2 & 96.75\% & 96.25\% & 97.22\% & 96.73\% & - \\ \hline \multirow{4}{*}{Att-Based-MFR (VGG-Face2\_m) [48]} & \multirow{4}{*}{2022} & LFW\_m & 99.41\% & 99.26\% & 99.56\% & 99.40\% & - \\ \cline{3-8}  & & AgeDB-30\_m & 95.38\% & 93.10\% & 98.11\% & 95.53\% & - \\ \cline{3-8}  & & CFF-F\_m & 96.98\% & 96.17\% & 98.40\% & 97.27\% & - \\ \cline{3-8}  & & MFR2 & 99.00\% & 99.50\% & 98.45\% & 99.02\% & - \\ \hline Fine-Tuned MobileNet [107] & 2022 & MadFaRe (12 subjects) & 78.41\% & - & - & - & - \\ \hline \multirow{4}{*}{ResNet-100-MR-MP(SRT) [47]} & \multirow{4}{*}{2022} & MRF & - & - & - & 0.8270 \\ \cline{3-8}  & & MRF2 & - & - & - & - & 3.4416 \\ \cline{3-8}  & & LFW & - & - & - & - & 0.9667 \\ \cline{3-8}  & & IJB-C & - & - & - & - & 2.9197 \\ \hline \multirow{4}{*}{ResNet-50-MR-MP(SRT) [47]} & \multirow{4}{*}{2022} & MRF2 & - & - & - & - & 1.1207 \\ \cline{3-8}  & & MRF2 & - & - & - & - & 6.2578 \\ \cline{3-8}  & & LFW & - & - & - & - & 1.2333 \\ \cline{3-8}  & & IJB-C & - & - & - & - & 3.0833 \\ \hline \multirow{4}{*}{MobileFaceNet-MR-MP(SRT) [47]} & \multirow{4}{*}{2022} & MRF2 & - & - & - & - & 3.1866 \\ \cline{3-8}  & & MRF2 & - & - & - & - & 7.8232 \\ \cline{3-8}  & & LFW & - & - & - & - & 2.2667 \\ \cline{3-8}  & & IJB-C & - & - & - & - & 4.6837 \\ \hline FaceNet + optimized SVM [108] & 2023 & CASIA + LWF + user dataset & 99.912\% & - & - & - & - \\ \hline Lightweight CNN [109] & 2024 & HMFD (frontal image) & 98.00\% & 98.00\% & 97.00\% & 98.00\% & - \\ \cline{3-8} \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._their contribution to the identification process. Additionally, [PERSON] [103] proposed a masked face recognition algorithm based on an attention mechanism for contactless delivery cabinets amid the COVID-19 pandemic. By leveraging locally constrained dictionary learning, dilated convolution, and attention mechanism neural networks, the algorithm aimed to enhance the recognition rates of masked face images. The model, validated on the RMFRD and SMFRD databases, demonstrated a superior recognition performance. Furthermore, the algorithm addressed occlusion challenges by constructing subdictionaries for occlusion objects, effectively separating masks from faces. The network architecture incorporated dilated convolution for resolution enhancement and attention modules to guide model training and feature fusion. Overall, the proposed approach offers promising advancements in masked face recognition, crucial for ensuring the safety and efficiency of contactless delivery systems. [PERSON] et al. [33] introduced the DeepMasknet model, a novel construction designed for face mask recognition and masked facial recognition.