# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 3 Masked Face Datasets
### Real Mask Datasets
Video recordings of individuals in various settings and poses were captured and subsequently segmented into frames every 0.5 s, each stored in individual folders. Careful manual curation was conducted to remove any obscured images, ensuring data quality. Ultimately, a collection of 2754 images representing 300 individuals was assembled. Figure 4 presents a selection of datasets specifically designed for Facial Mask Recognition (FMR), each offering samples showcasing correctly masked, incorrectly masked, and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Size** & **Identities** & **Access** & **Aim** & **Year** \\ \hline MAFA [19] & 30,811 & 3 & Public & FMR & 2017 \\ \hline MD (Kaggle) [20] & 853 & 3 & Public & FMR & 2019 \\ \hline RMFRD [17] & 5000/90,000 & 525 & Public & MFR & 2020 \\ \hline MFR2 [7] & 269 & 53 & Public & MFR & 2020 \\ \hline MASR-REC [21] & 11,615 & 1004 & Private & MFR & 2020 \\ \hline MFI [22] & 4916 & 669 & Private & MFR & 2020 \\ \hline MFV [22] & 400 & 200 & Private & MFR & 2020 \\ \hline MFDD [23] & 24,771 & 2 & Private & FMR & 2020 \\ \hline FMD (AIZOOTech) [24] & 7971 & 2 & Public & FMR & 2020 \\ \hline Moxa3K [25] & 3000 & 2 & Public & FMR & 2020 \\ \hline FMLD [26] & 41,934 & 3 & Public & FMR & 2021 \\ \hline [PERSON]’s custom dataset [27] & 7500 & 2 & Public & FMR & 2021 \\ \hline [PERSON]’s practical dataset [28] & 4672 & 3 & Private & FMR & 2021 \\ \hline ISL-UFMD [29] & 21,316 & 3 & Public & FMR & 2021 \\ \hline PWMFD [30] & 9205 & 3 & Public & FMR & 2021 \\ \hline WMD [31] & 7804 & 1 & Public & FMR & 2021 \\ \hline WMC [31] & 38,145 & 2 & Public & FMR & 2021 \\ \hline COMASK20 [32] & 2754 & 300 & Public & MFR & 2022 \\ \hline MDMFR (FMD) [33] & 2896 & 226 & Public & MFR & 2022 \\ \hline MDMFR (MFR) [33] & 6006 & 2 & Public & FMR & 2022 \\ \hline TFM [34] & 107,598 & 2 & Private & FMR & 2022 \\ \hline BAFMD [35] & 6264 & 2 & Public & FMR & 2022 \\ \hline FMDD [18] & 300,988 & 75,247 & Private & MFR & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of widely used benchmark datasets featuring real masks. unmasked faces. For instance, the Masked Face Detection Dataset (MFDD) [23] encompasses 24,771 images of masked faces. This dataset is compiled using two approaches: the first involves integrating data from the AI-Zoo dataset [24], which itself is a popular FMR dataset comprising 7971 images sourced from the WIDER Face [36] and MAFA [19] datasets. The second approach involves gathering images from the internet. MFDD serves the purpose of determining whether an individual is wearing a mask. The MAFA dataset [19] stands out as a prominent solution to the scarcity of large datasets featuring masked faces. Comprising 30,811 internet-sourced images, each possessing a minimum side length of 80 pixels, MAFA offers a substantial resource for research and development in MFR. The dataset contains a total of 35,806 masked faces, with the authors ensuring the removal of images featuring solely unobstructed faces to maintain the focus on occluded facial features. [PERSON], [PERSON], et al. [33] introduced a comprehensive dataset named the Mask Detection and Masked Facial Recognition (MDMFR) dataset, aimed at evaluating the performance of both face mask recognition and masked facial recognition methods. The dataset comprises two distinct parts, each serving a specific purpose. The first part is dedicated to face mask recognition and includes 6006 images, with 3174 images featuring individuals wearing masks and 2832 images featuring individuals without masks. Conversely, the second part focuses on masked facial recognition and contains a total of 2896 masked images representing 226 individuals. Figure 3c provides visual samples of both categories, with samples of masked and unmasked faces displayed on the left and masked face samples of three different individuals shown on the right. Researchers continue to leverage existing large datasets like MAFA and Wider Face to develop new datasets tailored to specific research objectives. [PERSON], [PERSON], et al.