ABSTRACT
Entire world including India is going through a pandemic that has arisen due to the outbreak of COVID-19. Medicines and Vaccine for Covid-19 are still under developmental stage. Wearing a Face Mask is the best viable option for humans to prevent the spread of infection due to Corona virus. As a result, controlling government agencies may want to know the percentage of people wearing masks during a period as well as which group of people are most likely to wear masks when they go outside. To help answer these questions, this paper introduces a model that can classify faces among masked faces and unmasked faces using Python 3.0 Language. In the present face detecting model, Vietnam based mask classifier dataset, CelebA dataset, WiderFace dataset and MAFA datasets are used for achieving better results. Single Stage Headless Face Detector (SSH) is successfully implemented to segregate human faces with or without mask. Experimental results with the Mask Classifier model show that it can achieve about 96.5% accuracy during testing stage. Selected on road going people video is tested successfully where the present model clearly segregated human faces with and without mask. The present model is useful to safeguard people from spread of Covid-19 virus in public places. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.