# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 3 Masked Face Datasets
### Synthetic Mask Datasets
[47] developed a synthetic dataset named MS1 MV2-Masked, derived from MS1 MV2 [49], incorporating various mask shapes and colors. They utilized Dlib [50] for extracting facial landmark points. However, they noted that Dlib encountered difficulties in extracting landmarks from 426 images. Consequently, their synthetic dataset comprises approximately 5374 images representing 85,000 identities. Building upon the FFHQ dataset [37], [PERSON], [PERSON], et al. [46] introduced a comprehensive synthetic dataset called MaskedFace-Net. This dataset comprises two primary sub-datasets: the Correctly Masked Face Dataset (CMFD) and the Incorrectly Masked Face Dataset (IMFD). These datasets serve as the sole categories within the main dataset dedicated to face mask recognition, collectively containing a total of 137,016 images. Notably, the dataset comprises 49% correctly masked faces (67,193 images) and 51% incorrectly masked faces (69,823 images). The SMFD dataset [43] comprises two distinct classes: one with a mask, encompassing 690 images, and the other without a mask, comprising 686 images. Consequently, the dataset contains a total of 1376 images, which were utilized to train a classification CNN network tasked with distinguishing between faces with masks and those without. Moreover, the Face Mask Detection Dataset [44] comprises 7553 images divided into two categories: with masks and without masks. Specifically, there are 3725 images depicting faces with masks and 3828 images featuring faces without masks. These images were sourced from the internet and encompass the entirety of images found in the SMFD dataset [43]. Thus, the dataset represents a hybrid composition, incorporating both real masked faces and synthetic masked faces. They employed this dataset to train their  
Figure 5: Samples of synthetic masked faces from benchmark datasets. model and also utilized a similar approach to create additional synthetic datasets based on Labeled Faces in the Wild (LFW) [6] and IARPA Janus Benchmark -C (IJB-C) [40]. It is common practice among authors to create synthetic datasets either manually or automatically using deep learning tools. Therefore, various face datasets can be considered or referenced in synthetic masked face datasets. One such dataset is the Celebrities in Frontal-Profile (CFP) dataset [38], comprising 7000 images representing 500 identities. This dataset is divided into two sub-datasets based on the face angle: frontal faces and profile faces, both depicting the same 500 identities. The frontal faces subset consists of 10 images per identity, while each identity in the profile faces subset is represented by 4 images. The AgeDB [39] dataset comprises 16,488 images featuring 568 prominent individuals, including actors/actresses, politicians, scientists, writers, and more. Each image comes annotated with identity, age, and gender attributes. Moreover, CelebA [5] stands out as one of the largest face datasets, boasting over 200,000 images spanning 10,000 identities. Complementing this dataset is CelebA-HQ [41], a high-resolution variant derived from CelebA, featuring 30,000 meticulously crafted high-quality images. Furthermore, leveraging the CelebA dataset, [PERSON] and [PERSON] [2] curated a Synthetic Masked Dataset comprising 30,000 images distributed across three subfolders: original unmasked faces, masked faces, and binary mask maps. Employing the MaskTheFace tool [7], they generated two masked datasets with varying sizes (256 and 512 pixels) tailored for face mask removal tasks. Additionally, [PERSON], [PERSON], et al. [3] engineered a synthetic dataset using Adobe Photoshop CC 2018, derived from CelebA, featuring 10,000 masked images alongside their original counterparts. [PERSON], [PERSON], et al. [45] introduced three composite datasets featuring mixed masked and unmasked faces. These datasets integrate original images with masked faces generated through their proprietary masked face image generation algorithm.