Artificial intelligence-based masked face detection: A survey

The COVID-19 virus is causing a global pandemic. The total number of new coronavirus cases worldwide by the end of November 2020 had already surpassed 60 million. The World Health Organization (WHO) has determined that wearing masks is a crucial precaution during the COVID-19 epidemic to limit the growth of viruses, and facemasks are frequently seen in public places worldwide. Also, many public service providers wear face masks (covering their mouths and noses). These events brought attention to the need for automatic computer-visionbased object detection (masked face detection) methods to track public behavior. Therefore, it is necessary to develop tools for monitor people who have not used masks in public service areas in real-time. Reducing the spread of infectious diseases can occur when masked face detection techniques are used for authentication instead of mask removal for face matching. A superior framework of masked face detection could improve security systems and lower the rate of crime. Masked face detection is a computer vision method standard in people's daily lives to recognize, discover, and recognize masked faces in pictures and videos. This study provides a thorough and systematic analysis of masked face detection algorithms. With the help of examples, we have thoroughly examined and reviewed the studies done concerning face mask identification and techniques for masked face detection. Additionally, we compared and explained different masked face detection dataset types, libraries, and techniques. We also discussed the challenges with masked face detection and whether the researchers could overcome them. We have discussed and conducted a thorough evaluation of the accuracy, pros, and cons of various approaches by comparing their performance on multiple datasets. As a result, this study aims to give the researcher a broader viewpoint to aid him in finding patterns and trends in masked face detection in various COVID-19 contexts, overcoming challenges that are still present, and creating future algorithms for masked face detection that are more reliable and accurate.