# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 1 Introduction  
In December 2019, a new and worryingly contagious primary atypical (viral) pneumonia broke out in Wuhan, China. The new disease, called COVID-19, was later found to be caused by a previously unknown zoonotic coronavirus, named SARS-CoV-2. To help limit the spread of this new coronavirus, the World Health Organization (WHO), medical experts as well as governments across the world now recommend that people wear face-masks if they have respiratory symptoms, if they are taking care of the people with symptoms or otherwise engage frequently with larger groups of people [1, 2, 3, 4]. In response to these developments, research in face-mask detection has attracted the attention of the computer vision community recently and initiated efforts towards developing automatic detectionmodels that can help society (through monitoring, screening and compliance-assessment applications) containing the COVID-19 pandemic [5]. Face-mask detection represents both a detection as well as a classification problem because it requires first the location of faces of people in digital images and then the decision of whether they are wearing a mask or not. The first part of this problem has been studied extensively in the computer vision literature, due to the broad applicability of face-detection technology [6]. The second part, on the other hand (i.e., predicting whether a face is masked or not), has only gained interest recently, in the context of the COVID-19 pandemic. Although a considerable amount of work has been done over the last year on this part [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], it typically only tries to detect whether a mask is present in the image. No special attention is given to whether the masks are properly placed on the face and are, hence, worn in accordance with the recommendations of medical experts. This limits the application value of existing face-mask detection techniques and warrants research into computer vision models capable of not only detecting the presence of facial mask in images, but also of determining if the masks are worn correctly. To further illustrate this issue, several sample images from the MAFA (MAsked FACes) dataset [18] are shown in Figure 1. MAFA represents one of the most popular datasets for training face-mask detectors, and in a similar manner to most other datasets publicly available for this purpose, contains only binary labels indicating whether face-masks are present in the images or not. As illustrated in Figure 1 masked facial images in such datasets typically belong to one of two groups: (i) faces with correctly worn masks (marked green), and (ii) faces with incorrectly worn masks (marked red). Because the _correctness_ (or _compliance_ with recommendations) of the mask placement is not annotated, existing mask detectors commonly learn from highly noisy data (considering the intended purpose of the detectors) and commonly do not flag faces where a mask is present, but does not cover the nose, mouth and chin. We note that this is a common issue seen across most of the existing work on face-mask detection [16; 19; 20; 21] and has important implications for the usefulness of the designed detectors in practice. In this paper, we try to address this shortcomming and focus on the problem of detecting properly placed face-masks. Towards this end, we construct a large experimental dataset of facial images from two publicly available donor datasets, i.e., MAFA [18] and Wider Face [22]. We select representative and challenging images from the donor datasets and use a semi-automatic procedure to partition them into groups of images with correctly and incorrectly placed face-masks. Labels for gender, ethnicity and pose are also added. Because face-mask detectors typically include two computational steps (i.e., detection and classification), we first use the constructed dataset to evaluate the performance of several recent face detectors with masked-face images. To the best of our knowledge, this problem has not been studied extensively in the literature before.