Performance Evaluation of Intelligent Face Mask Detection System with various Deep Learning Classifiers - C.Jagadeeswari, M.Uday Theja (International Journal of Advanced Science and Technology, 2020)
The continuous spread of Corona virus has led to sustained increase in the mortality rate of many countries across the globe from the day it started in Wuhan, China. At this moment, when there is no medicine or vaccine, the World Health Organization has suggested the use of surgical/medical masks to mitigate the spread of Virus. As per WHO, use of masks is made mandatory in communities, largely crowded areas, during home care and in health care settings in areas which have reported Covid-19 cases. Wearing of masks during, as well as post- pandemic would be of paramount importance until a vaccine is invented. Such a measure during the COVID-19 pandemic has received varying recommendations from different public health agencies and governments. In order to ensure safe mitigation of the spread of virus, the detection of violators is highly desirable. This paper highlights the use of deep learning approaches to identify people who do not wear mask. The System is trained to identify accurately whether a person is wearing mask or not. When the algorithm identifies person without mask, an alarm should be generated to alert the people around or the concerned authorities nearby, so that necessary actions can be taken against such violators. As most of the institutions, companies, industries, malls, hospitals, have to start operating with few relaxations before this pandemic is completely erased, integrating face mask detection system with the existing access control system at entry and exit points is highly recommended. Not just for Covid19 pandemic, where ever and whenever facemask is mandated to mitigate any air-borne diseases, entry and exit access systems can be integrated with such technology to help in reducing the spread of virus. To come up with a system to be implemented in large scale, different classifiers with different optimizers need to be evaluated. This paper shows a comparison of classifiers like MobileNetV2, RESNET50, VGG16 each with Optimizers ADAM, ADAGRAD, and SGD.