# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 3 Masked Face Datasets
### Real Mask Datasets  
In the domain of MFR and FMR, datasets that incorporate genuine face masks provide crucial insight into the complexities posed by real-world scenarios. These datasets meticulously capture the intricacies of diverse face masks worn by individuals across various settings, ranging from public spaces and workplaces to social gatherings. The authenticity embedded in these masks significantly enhances the realism of the training process, allowing models to effectively adapt to the challenges presented by authentic face coverings. Notably, addressing a challenge highlighted in the discussion of limitations--the scarcity of real masked face datasets, especially those utilized in face mask removal tasks where pairing masked and unmasked faces is essential--this subsection looks into prominent benchmark datasets featuring real face masks. Through an exploration of these datasets, we illuminate their characteristics, applications, and significance in propelling advancements within the field of MFR methodologies. Table 1 offers an overview of the primary benchmark datasets utilized in both MFR and FMR, elucidating their essential attributes and utility across various applications. Furthermore, Figure 2(a) showcases sample images from the RMFRD dataset, recognized as the most extensive dataset utilized for MFR. For comparison, Figure 2(b) displays samples from another significant dataset in the MFR domain, namely MFR2 (2017). Meanwhile, Figure 4 illustrates sample images from datasets employed in FMR, offering visual insights into the various real mask datasets discussed. In the realm of real face mask datasets, the Real-World Masked Face Recognition Dataset (RMFRD) [17] stands out as one of the most extensive publicly available resources for MFR. RMFRD comprises 90,000 unmasked faces and 5000 masked faces for 525 individuals, serving as a valuable asset for training and evaluating MFR models. Figure 3a showcases samples from RMFRD, illustrating examples of both masked and unmasked faces for various individuals, thereby facilitating MFR applications and serving as ground truth data for training face mask removal models. Another notable dataset is the Face Mask Recognition Dataset [18], a large private repository comprising 300,988 images of 75,247 individuals. Each individual in this dataset is represented by four selfie images: one without a mask, one with a properly masked face, and two with incorrectly masked faces. These diverse scenarios offer rich training data for face mask recognition models. Additionally, MFR2 [7] is a popular dataset in the field, containing 269 images for 53 identities,  
Figure 4: Samples from real masked face datasets used in face mask recognition. Figure 3: Samples of masked and unmasked faces from the real-mask masked face datasets used in masked face recognition. typically used to evaluate model performance trained on either real masked datasets or large synthetic datasets. Figure 3b showcases samples from MFR2. The Masked Face Segmentation and Recognition (MFSR) dataset [21] comprises two components. The first part, MFSR-SEG, contains 9742 images of masked faces sourced from the Internet, each annotated with manual segmentation labels delineating the masked regions. These annotations are particularly useful in FU tasks, serving as an initial step, as seen in stage 1 of GANMasker [2]. The second part, MFSR-REC, encompasses 11,615 images representing 1004 identities, with 704 identities sourced from real-world collections and the remaining images gathered from the Internet. Each identity is represented by at least one image featuring both masked and unmasked faces. [PERSON], [PERSON], et al. [22] introduced two datasets to assess MFR models. The first dataset, known as Masked Face Verification (MFV), includes 400 pairs representing 200 distinct identities. The second dataset, Masked Face Identification (MFI), comprises 4916 images, each corresponding to a unique identity, totaling 669 identities. The COMASK20 dataset [32] employed a distinct approach.