# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 8 Conclusions  
This survey paper has conducted a thorough investigation into recent advancements and challenges within the realms of masked face recognition (MFR), face mask removal (FMR), and face unmasking (FU). By examining various methodologies, we have identified several significant strides made in these areas, particularly in addressing the occlusion challenges posed by masks and improving the generalization of models to different masked face conditions.  
Throughout our exploration, we have highlighted the importance of refining synthetic data generation techniques, incorporating real-world masked datasets, and improving the performance of deep learning methods tailored to MFR and FU tasks. Researchers have also made notable advancements by developing innovative techniques for extracting and reconstructing unmasked facial features from masked images, especially through deep learning models like GANs and diffusion-based architectures.  
While substantial progress has been made, the limitations identified indicate that there are still considerable gaps that need to be addressed to ensure these technologies achieve reliable, real-time performance across diverse scenarios. Ethical and privacy concerns also remain a key aspect that must be considered as these technologies evolve and find broader applications.  
Conceptualization, M.M.; methodology, M.M.; software, M.M.; validation, M.M., M.S.K. and H.-S.K.; formal analysis, M.M., M.S.K. and H.-S.K.; investigation, M.M. and H.-S.K.; resources, M.M. and H.-S.K.; data curation, M.M. and M.S.K.; writing--original draft preparation, M.M.; writing--review and editing, M.M., M.S.K. and H.-S.K.; visualization, M.M. and H.-S.K.; supervision, M.M. and H.-S.K.; project administration, H.-S.K.; funding acquisition, H.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (No. 2023R1A2C1006944, 50%) and partly by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2024-2020-0-01462, 50%).  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
MFR Masked face recognition  
FMR Face mask recognition  
FU Face unmasking  
GANs Generative adversarial networks  
SSPS Single sample per subject  
RMFRD Real-World Masked Face Recognition Dataset  
MFSR Masked Face Segmentation and Recognition Dataset  
MFV Masked Face Verification  
MFI Masked Face Identification  
MFND Masked Face Detection Dataset  
MDMFR Mask Detection and Masked Facial Recognition Dataset  
FMLD Face-Mask Label Dataset  
ISL-UFMD Interactive Systems Labs Unconstrained Face Mask Dataset  
MD-Kaggle Masked Dataset  
PWMFD Properly Wearing Masked Face Detection Dataset  
WMD Wearing Mask Detection  
WME Wearing Mask Classification  
BAFMD Bias-Aware Face Mask Detection  
SMFRD Simulated Mask Face Recognition Dataset  
CMFD Correctly Masked Face Dataset  
IMFD Incorrectly Masked Face Dataset  
LFW Labeled Faces in the Wild  
IJB-C IARPA Janus Benchmark-C  
CFP Celebrities in Frontal-Profile  
ERR Error rate  
ROC Receiver Operating Characteristic Curve  
TP True positive  
TN True negative  
FP False positive  
FN False negative  
AUC Area Under the Curve  
FAR False acceptance rate  
FRR False rejection rate