ABSTRACT
To stem the COVID19 pandemic, great attention is needed to mitigate public health and the global economy which are negatively impacting. To overcome this, a technique is required to urge people put on the face mask. To contribute to the health of communities, this article aims to design a very precise and real-time analysis that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. According to the World Health organization, the most effective way to fight the transmission of the corona virus is to wear medical masks. The detection of face mask in is done with the machine learning by using the series of stages involved through classification of images: MobileNetV2. The steps and stages used for developing the model square measure grouping the information, and pre-processing the data to remove noisy data, splitting the data, testing the model for the accuracy, and implementation of the model. The engineered model will sight those that square measure sporting a mask associated not sporting it at an accuracy of 95.85 percent. © 2022 IEEE.