# How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
## 2 Related Work
### Mask-Detection Methods  
Multiple solutions for detecting masked-face images have been presented in the literature or made available online over the last year. Motivated by the success of the RetinaFace face detector [29], for example, the so-called RetinaFace AntiCov model was introduced in [19]. The model uses a MobileNet backbone and can detect whether or not faces are masked in the detected results. Similar work using another backbone model was also presented by [PERSON] et al. in [30]. [PERSON] and [PERSON] [21] proposed a one-stage face-detection model capable of classifying detected faces with respect to whether they are wearing masks or not. The proposed approach was again inspired by the RetinaNet model and represents a one-stage object detector that consists of a Feature Pyramid Network (FPN) and a novel context attention module. The model comprises a backbone, a neck, and a head. The main (high accuracy) model uses a ResNet backbone, but a simpler model with a MobileNet backbone is also explored. For the neck of the model (the intermediate connection between the backbone and the heads of the model), the authors use an FPN. For the heads, the proposed approach relies on a structure similar to that used in single-stage detectors (SSD). The model is tested on selected subsets from the MAFA and Wider Face datasets that consist of a total of 7959 images with masked and unmasked faces. Despite the impressive detection performance the proposed models do not distinguish between faces that wear masks properly (in accordance with recommendations) and faces that do not. The closest to our work is the recent paper by [PERSON] and [PERSON] [31]. Here, the authors describe an approach (SRCNet) for classifying face-mask wearing. The approach incorporates an image super resolution model that makes it possible to process low-resolution faces and a classification network that predicts whether faces are masked, without masks or if the masks are worn incorrectly. The model is trained and evaluated on a dataset that contained a total of 3835 images, which unfortunately is no longer available. Out of the 3835 images, 671 contain faces without masks, 134 images contain faces with incorrectly worn masks and 3030 images contain faces with correctly worn face-masks. An accuracy of 98.70% is reported for the proposed model. Although this work shares the basic problem statement, we do not focus solely on low-resolution faces, but explore the general task of detecting whether face-masks are worn correctly or not regardless of the data characteristics. There are also some commercial solutions available that offer face-mask detection [32; 33]. The face-mask detection feature offered by these solutions allow the simple plugging in of a video stream from a selected (surveillance) camera and then the use of vision techniques to monitor crowds and generate alerts when detecting people without masks. Baidu, on the other hand, offers an open-source tool to detect faces without masks [34]. The Baidu PaddleHub mask-detection model is based on PyramidBox, a context-assisted single-shot face detector published in 2018 [35]. The model is open-sourced to help advance the field and help to prevent and control the pandemic.