# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 5 Masked Face Methods
### Face Unmasking Approaches
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & **Year** & **Dataset** & **PSNR** & **SSIM** & **FID** & **NIQ** & **BRISQUE** & **MAE** & \(L_{1}\) & \(L_{2}\) \\ \hline Context Encoders [78] & 2016 & Paris StreetView & 18.58 dB & - & - & - & - & - & 9.37\% & 1.96\% \\ \hline GFC (M5 and Q5) [84] & 2017 & CelebA & 19.5 & 0.784 & - & - & - & - & - \\ \hline PConv (N/B) [81] & 2018 & Places2 & 18.21/19.04 & 0.468/0.484 & - & - & - & - & 6.45/5.72 & - \\ \hline \multirow{2}{*}{FFII-GatedCon [80]} & \multirow{2}{*}{2019} & Places2(rectangular mask) & - & - & - & - & - & - & 8.6\% & 2.0\% \\ \cline{2-10}  & & Places2(free-form mask) & - & - & - & - & - & 9.1\% & 1.6\% \\ \hline EdgeConnect [83] & 2019 & Places2 & 21.75 & 0.823 & 8.16 & - & - & - & 3.86 & - \\ \hline MRGAN [85] & 2019 & Synthetic dataset & 29.91 dB & 0.937 & - & 3.548 & 29.97 & - & - & - \\ \hline ERFCOFI (Mask) [86] & 2020 & CelebA and CelebA-HQ & 28.727 & 0.908 & - & 4.425 & 40.883 & - & - & - \\ \hline GUMF [3] & 2020 & CelebA & 26.19 dB & 0.864 & 3.548 & 5.42 & 37.85 & - & - & - \\ \hline \multirow{3}{*}{R-MNet-0.4 [82]} & \multirow{3}{*}{2021} & CelebA-HQ & 40.40 & 0.94 & 3.09 & - & - & 31.91 & - & - \\ \cline{1-1} \cline{3-10}  & & Paris Street View & 39.55 & 0.91 & 17.64 & - & - & 33.81 & - & - \\ \cline{1-1} \cline{3-10}  & & Places2 & 39.66 & 0.93 & 4.47 & - & - & 27.77 & - & - \\ \hline GANMasker [2] & 2023 & CelebA & 30.96 & 0.95 & 16.34 & 4.46 & 19.27 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of GAN-based face mask removal and image inpainting in general. ing Network (R-MNet) for face inpainting and free-face mask completion. Similarly, [PERSON] et al. [85] leveraged a GAN-based network to effectively remove microphones from facial images. [PERSON] et al. [84] devised a GAN architecture tailored specifically for generating missing facial components such as eyes, noses, and mouths. Further expanding the capabilities of inpainting, [PERSON] et al. [86] introduced a two-stage GAN framework enabling users to selectively remove various objects from facial images based on their preferences, with the flexibility to remove multiple objects through iterative application. After the COVID-19 pandemic, considerable attention has been directed towards the development of techniques for face mask removal, encompassing both GAN-based and diffusion-based approaches. Among the GAN-based methodologies, [PERSON] [2] introduced a two-stage network architecture, initially focusing on face mask region detection to guide the subsequent inpainting stage. Additionally, [PERSON] enhanced their results by integrating Masked-unmasked region Fusion (MURF) mechanisms. Furthermore, [PERSON] et al. [3] proposed a GAN-based network specifically designed for removing masks from facial images. Conversely, within the realm of diffusion models, [PERSON] et al. [87] introduced the RePaint method, which utilizes a DDPM [91] foundation for image inpainting tasks. Similarly, [PERSON] et al. [88] proposed the COPAINT method, enabling coherent inpainting of entire images without introducing mismatches. Broadly addressing image restoration, [PERSON] et al. [89] presented the Denoising Diffusion Restoration Model (DDRM), offering an efficient, unsupervised posterior sampling approach for various image restoration tasks. In a related context, [PERSON] et al. [90] devised the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework applicable to diverse linear image restoration problems, including image super-resolution, inpainting, colorization, compressed sensing, and deblurring.