Abstract
The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using TensorFlow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.
Highlights
The COVID-19 pandemic has become a major threat to both global health and the economy
Xiong et al [23] have worked on the concept of machine learning and have shown its importance to validate the self-generated dataset from multiple scenarios, in converses the effective use of the information to assist in abnormal object detection based on the Mask R-Convolution Neural Network (CNN) approach
The communicable disease including COVID-19 can be prevented to a greater extent by wearing a mask or covering the face, protecting the users and the others around
Summary
The COVID-19 pandemic has become a major threat to both global health and the economy. Apart from maintaining physical distancing and washing hands frequently, the proper use of facemasks has emerged as one of the pillars for preventing community transfer of the disease. Other measures to eradicate the virus includes physical distancing, avoiding crowd, proper ventilation, cleaning or sanitizing hands, taking steam to kill the virus in the initial stages before it enters the lungs, covering the face while sneezing, coughing, and maintaining hygiene [1,7]. During periodic infection outbreaks in Japan, it was observed that the use of facemasks by children in the age group of 9–12 years were effective [8]. Singhal et al [12] say that viruses will not disappear soon as their different variants keep on emerging In this scenario modified anatomical face mask (M-AFM) serves as an effective alternative to the N95 respirator, HME+ by filters are disposable and have a filtration efficiency of 99.99% [13,14]. Germany suggested a 40% reduction in the daily growth rate of COVID-19 cases with masks [15]
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