# A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness
## 1 Introduction  
Human-to-human transmission plays an essential role in the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2, 2019-nCoV), causing the disease named COVID-19 [1, 2]. It is known that one of the principal ways of SARS-CoV-2 transmission is through tiny droplets ejected from a pre-symptomatic patient while sneezing, coughing, or just speaking. Therefore, the use of face masks was recommended by the World Health Organization (WHO), especially in public areas, to reduce the rates of virus spreading [3]. The mandatory use of face masks varies over countries; for instance, due to the arrival of COVID-19 vaccines and the low contagion rate, some countries such as Israel have waived obligatory face mask use [4]. However, some countries still have high rates of COVID-19 cases, and a significant part of the population is not vaccinated yet, making the use of face masks a mandatory rule [5, 6, 7, 8]. These countries are facing the challenge of creating a preventive system to detect the incorrect use (or the absence) of face masks.  
In general, monitoring people who do not respect the use of face masks is a challenging task, especially in crowded public areas where it becomes impossible to track whether everyone is using face masks according to the WHO recommendations. For this reason, there is great need for a computer system that enables the automatic monitoring of correct/incorrect face mask use. Currently, computer vision (CV) tasks are useful for solving problems related to object detection, classification, object counting, visual surveillance, etc., taking advantage of video resources from public surveillance cameras located in many public areas (i.e., shopping malls, supermarkets, airports, train stations, stadiums, etc.) [9; 10; 11; 12]. The problem of the correct/incorrect wearing of face masks implies two CV tasks: (1) object detection and (2) object classification. The object detection task is helpful for finding the faces of people in images or videos, and the object classification task divides the faces detected by the object locator into different classes (e.g., correct or incorrect use of face masks).  
CV is an exciting approach that provides the necessary tools to build systems capable of detecting and classifying the correct/incorrect use of face masks. However, to increase the accuracy of the detection and classification models, CV researchers are needed to help in the development of models that enhance the existent systems. In the last year, researchers in the CV community have worked hard to propose deep learning (DL) models to tackle this vital area for the benefit of society's healthcare [13; 14; 15; 16]. However, a significant part of the research is focused on generating suitable datasets to train the models, especially for the detection and classification of occluded faces. Therefore, it is important to address various approaches to the classification for COVID-19 face mask wearing in order to produce an accurate DL model.  
In this work, we propose a compound convolutional neural network (CNN) pipeline for COVID-19 face mask detection and classification. In addition, by conducting a grid search among the optimizer and cost function hyperparameters, we present the best configuration that optimizes the model in terms of classification accuracy. Moreover, a new cleaned dataset is presented to train the object classification model. To enable reproducible research, we have used the public and open Face Mask Label Dataset (FMLD) [17] and Medical Mask Dataset (MMD) [18] to train the classifier with distinct DL models.