# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 2 Related Work
### Face-Mask Datasets  
Prior to the COVID-19 pandemic, computer vision problems related to masked faces received only limited attention from the vision community. Instead, masked faces were typically studied within a broader problem domain related to processing and classifying occluded facial images [25]. As a result, few datasets with annotated masked faces were available publicly for research purposes. However, driven largely by the COVID-19 pandemic, such datasets started to appear recently. [PERSON] et al. [5], for example, introduced three datasets to improve the performance of face-recognition methods with masked faces. These include the Masked-Face Detection Dataset (MFDD), the Real-world Masked-Face Recognition Dataset (RMFRD) and the Simulated Masked-Face Recognition Dataset (SMFRD). According to the authors, MFDD contains 24,771 masked faces and is an extended version of the dataset initially presented in [20]. The images in MFDD were crawled from the Internet and, therefore, contain faces captured in challenging conditions and in real-world settings. The dataset is labeled according to whether people wear masks and is primarily intended for training and evaluating face-mask detectors. The RMFRD includes 5000 images of 525 people with masks, and 90,000 images of the same 525 subjects without masks. Images labeled as wearing masks contain faces with correctly worn masks, but also faces where the masks are not placed correctly and, hence, do not conform to the recommendations issued during the COVID-19 pandemic. The SMFRD dataset is a simulated dataset of masked-face images. It contains 500,000 face images of 10,000 subjects, where facial masks were added artificially to simulate masked faces. It needs to be noted that only a subset of the images in these datasets is publicly available for download, as summarized in Table 1.  
[PERSON] et al. [18] presented the Masked Faces (MAFA) dataset (the term _masked_ is used here to indicate general types of occlusions, not necessarily masks), a (masked/occluded) face-detection dataset, collected from the Internet. MAFA contains 30,811 images with real-world appearance variability. Images in the dataset are labeled according to the degree and type of occlusion, but not with respect to whether facial masks are placed properly or not--see Figure 2 for an illustration. The MAFA dataset is also used often for research on face-mask detection together with datasets of unmasked faces, e.g., [20; 21], but due to the annotation problem mentioned above, this typically results in detection techniques capable of detecting general occlusions rather than facial masks. Although the datasets presented above do not distinguish between masked faces with correctly and incorrectly worn masks, there are two smaller datasets on the Kaggle portal that do make the distinction [26; 27]. The first, the Face-Mask Detection (FMD) dataset [26], contains 853 images that are divided into three classes: (i) with mask, (ii) without mask and (ii) mask worn incorrectly. A total of 4072 faces are annotated in these images (with mask: 3232, without mask: 717 and mask worn incorrectly: 123). However, the main problem with this dataset is that most of the faces are very small, which limits the use of the dataset for the development and evaluation of face-mask detectors. The second dataset from Kaggle [27] consists of 6024 images acquired in unconstrained settings. The dataset was constructed with particular attention to diversity, featuring people of different ethnicities, ages, and regions. Images are labeled with respect to 20 classes, including faces with mask, faces with masks incorrectly worn, faces without masks, covered faces and 16 other classes for masks, face shields, hats, scarves, and other accessories. Out of the 6024 images, 4326 are annotated and available on Kaggle for training. The remaining (sequestered) images were later annotated in the form of a standalone dataset called Medical Mask Dataset (MMD) [28].