Deep learning for face mask detection: a survey

Abstract

The Coronavirus Disease (Covid-19) was declared as a pandemic by WHO (World Health Organization) on 11 March 2020, and it is still currently going on, thereby impacting tremendously the whole world. As of September 2021, more than 220 million cases and 4.56 million deaths have been confirmed, which is a vast number and a significant threat to humanity. Although, As of 6 September 2021, a total of 5,352,927,296 vaccine doses have been administered, still many people worldwide are not fully vaccinated yet. As stated by WHO, “Masks” should be used as one of the measures to restrain the transmission of this virus. So, to reduce the infection, one has to cover their face, and to detect whether a person’s face is covered with a mask or not, a “Face mask detection system” is needed. Face Mask Detection falls under the category of “Object Detection,” which is one of the sub-domains of Computer Vision and Image Processing. Object Detection consists of both “Image Classification” and “Image Localization”. Deep learning is a subset of Machine learning which, in turn, is a subset of Artificial intelligence that is widely being used to detect face masks; even some people are using hybrid approaches to make the most use of it and to build an efficient “Face mask detection system”. In this paper, the main aim is to review all the research that has been done till now on this topic, various datasets and Techniques used, and their performances followed by limitations and improvements. As a result, the purpose of this study is to give a broader perspective to a researcher to identify patterns and trends in Face mask detection (Object Detection) within the framework of covid-19.