# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 3 Masked Face Datasets
### Synthetic Mask Datasets  
However, the accessibility of synthetic masked face datasets has seen a significant boost due to the abundance of public face datasets available for generation purposes. In contrast to datasets featuring authentic masks, synthetic mask datasets bring a unique perspective by incorporating artificially generated face coverings. These datasets offer a controlled environment, enabling researchers to explore various synthetic mask variations, including considerations such as style, color, and shape. The controlled nature of these datasets facilitates a systematic exploration of challenges in MFR, providing valuable insights into the response of models to different synthetic masks. Within this subsection, we look into benchmark datasets featuring synthetic masks, examining their creation processes, advantages, and potential applications in the field of masked face recognition and related tasks. Figure 5 showcases samples from some datasets with synthetic masks, while Table 2 provides a detailed summary of these synthetic datasets discussed in this subsection. As highlighted in the preceding subsection, RMFRED stands out as one of the most expansive real-mask masked face datasets. [PERSON], [PERSON], et al. [23] took a different approach by automatically applying masks to face images sourced from existing public datasets such as CASIA-WebFace [4], LFW [6], CFP-FP [38], and AgeDB-30 [39]. This effort resulted in creating a Simulated Mask Face Recognition Dataset (SMFRD), comprising 536,721 masked faces representing 16,817 unique identities. [PERSON] and [PERSON] [7] introduced MaskTheFace, an open-source tool designed to mask faces in public face datasets effectively. This tool was utilized to generate large masked face datasets like LFW-SM, which contains 64,973 images representing 5749 identities. It incorporates various types of masks, including surgical-green, surgical-blue, N95, and cloth, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Size** & **Identities** & **Access** & **Aim** & **Year** \\ \hline LFW [6] & 13,233 & 5749 & Public & MFR & 2008 \\ \hline CASIA-WebFace [4] & 494,414 & 10,575 & Public & MFR & 2014 \\ \hline CelebA [5] & +200,000 & 10,000 & Public & FMR and FU & 2015 \\ \hline CFP [38] & 7000 & 500 & Public & MFR & 2016 \\ \hline AgeDB [39] & 16,488 & 568 & Public & MFR & 2017 \\ \hline IJB-C [40] & 148,876 & 3531 & Public & MFR & 2018 \\ \hline CelebA-HQ [41] & 30,000 & - & Public & FMR and FU & 2018 \\ \hline VGGFace2 [42] & 3.31 M & 9131 & Public & MFR & 2018 \\ \hline SMFRD [23] & 536,721 & 16,817 & Public & MFR & 2020 \\ \hline LFW-SM [7] & 64,973 & 5749 & Public & MFR & 2020 \\ \hline VGGFace2-mini-SM [7] & 697,084 & 8631 & Public & MFR & 2020 \\ \hline SMFD(GitHub) [43] & 1376 & 2 & Public & FMR & 2020 \\ \hline FMDD(Kaggle) [44] & 7553 & 2 & Public & FMR & 2020 \\ \hline PS-CelebA [3] & 10,000 & - & Private & FU & 2020 \\ \hline VGGFace2\_m [45] & 666,800 & 8335 & Public & MFR & 2021 \\ \hline LFW\_m [45] & 26,466 & 85,749 & Public & MFR & 2021 \\ \hline CF\_m [45] & 5000 & 500 & Public & MFR & 2021 \\ \hline MaskedFace-Net [46] & 137,016 & 2 & Public & FMR & 2022 \\ \hline MS1 MV2-Masked [47] & 5.374 M & 85,000 & Public & MFR & 2022 \\ \hline CASIA-WebFace\_m [48] & 789,296 & 10,575 & Public & MFR & 2022 \\ \hline Synthetic-CelebA [2] & 30,000 & - & Private & FU & 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of popular synthetic mask benchmarking datasets. derived from the original LFW dataset [6]. Utilizing the same tool, they developed another masked dataset called VGGFace2-mini-SM [7], extracted from the original VGGFace2-mini dataset, a subset of the VGGFace2 dataset [42], with 42 images randomly selected per identity. This augmentation expanded the total image count to 697,084, maintaining the same 8631 identities. Various mask types, akin to those employed in LFW-SM, were integrated into this dataset. [PERSON], [PERSON], et al.