# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 3 The Face-Mask Label Dataset (FMLD)
### Dataset Construction  
The first step in the construction of the FMLD dataset is the selection of suitable images for annotation that are reasonably balanced across different categories (e.g., masked vs. not masked, male vs. female), and representative of real-world imaging conditions, where appearance variability across lighting, pose, image quality and other similar factors is expected. In a similar manner to [20], we identify the MAFA and Wider Face datasets as suitable donors and select images from these datasets for labeling.  
* _Selection of images with masked faces_: Masked faces are taken from the MAFA dataset only. MAFA has a total of 30,811 images that contain 35,806 occluded faces and 991 unoccluded faces. Faces in the dataset come in various poses. At least one part of each face is typically occluded, mostly by some sort of mask. A few example images from the MAFA are shown in Table 2, including faces with different types and degrees of occlusion--these are labeled in the dataset. As can be seen, some faces are masked by hands or other objects rather than physical masks, and are, hence, not useful for studying face-mask detection problems. For FMLD, we first identify candidate images for the group with _correctly placed masks_. For this group, we consider faces that have an occlusion of type 1 or 2 and an occlusion degree of 3 (illustrated with the first two images in the last row of Table 2 with a green border). Such faces typically have masks that cover the mouth, nose and chin. For the group of _incorrectly worn masks_, we consider faces with occlusions of type 1 and an occlusion degree of 1 or 2. The rest of the MAFA faces are added to the group of faces without masks. The presented prefiltering generates a fairly clean list of candidate images for FMLD, but still results in several incorrectly classified faces. We, therefore, inspect all images manually and modify incorrect annotations. The main criterium for annotating faces as compliant was that the nose, mouth, and chin were covered, even if the occluding object was not necessarily a mask, but something akin to a scarr or buff. The complete statistics of the MAFA part of FMLD are given in Table 3.
* _Selection of face images without masks_: Because MAFA contains many faces with masks, additional images of faces without masks are needed to balance the classes. Thus, we include images from the Wider Face-detection benchmark [22] in FMLD. Specifically, 11,123 images are selected that contain 26,278 faces with a high degree of variability in scale, pose and occlusion. No bounding box ground-truth is available for the test images, so we only consider images from the training and validation sets of Wider Face. We select images with faces that are at least 40 pixels in size \((min(width,height)>40)\) and exclude faces that are marked as invalid, areheavily blurred, have heavy occlusions and atypical poses. We also omit the entire _\"30--Surgeons\"_ group because most of the faces contain masks. Through this prefiltering we generate an initial candidate list of faces without masks that are then manually inspected for the presence of facial masks. The presented procedure results in the overall statistics presented in Table 3 for the Wider Face part of FMLD.  
We partition the selected images into a training and testing set that can be used in standardized experiments to evaluate and compare face-mask detection techniques. A summary of the selection is again presented in Table 3.