There is a need to wear a mask during the coronavirus outbreak to efficiently deter the transmission of COVID-19 virus. In these instances, traditional facial screening technologies obsolete for monitoring of group entry at Airports, shopping malls, railway stations, etc. It is, therefore, vital to boost the efficiency of screening. This paper addresses the machine learning algorithm for contactless face screening systems in group participation, social interaction, school management, mall entry management, and market resumption scenarios in the case of COVID- 19. A method to screen entry with masks are developed using machine learning, which depends on various face specimens that were discussed here. The second fold discussion in this paper is that previously there are not many freely accessible masked face-databases. To this end, various forms of masked face data sets are identified, namely MFDD, Real MFRD, and Simulated MFRD. Such data sets became widely accessible to businesses and academics, based on which specific apps may be built on masked faces. The mathematical model, with the code was given. The availability and issues of the above data sets were discussed for the benefit of researchers.