Face Mask Detection using Machine Learning
Abstract - Coronavirus disease 2019 has affected the world seriously. Coronavirus belongs to the class of viruses A coronavirus identified in 2019, SARS-CoV-2, has caused a pandemic of respiratory illness, called COVID-19. COVID-19 is the disease caused by SARS-CoV-2, the coronavirus that emerged in December 2019. COVID-19 can be severe, and has caused millions of deaths around the world as well as lasting health problems in some who have survived the illness. Coronavirus spreads through droplets and virus particles released into the air when an infected person breathes, talks, laughs, sings, coughs or sneezes. After sneezing or coughing the large droplets that fall into the ground causes the viruses to accumulate at one places and this leads to transfer of disease from person to person. Wearance of mask prevents the spread of such infections and disease. Mask causes the hinderance to the virus to enter through the nose and throats through that accumulated place that resulted into the deposition of virus from sneezing or coughing. Due to this the govt has made mandatory to follow the proper wearance of the mask in public. But achieving this practice manually, it is a very tedious task. Therefore, this demands the existence of automated face mask detection system, that identifies automatically whether the person has wore a mask or not. The detector system should be viable and has to deployable in public so as to curb the spread of disease thereby making the public to mandatorily wear the mask. In this paper we aim to perform a comparative analysis of various sophisticated image classifiers based on deep learning, in terms of vital metrics of performance to identify the effective deep learning based model for face mask detection.
Keywords - Facemask Detection, RMFD, SMFD VGG-16, MobileNetV2, InceptionV3.