Authors - Mangal Patil, Jyoti Morbale, Anuradha Nigade, Saloni, Padma Priya Abstract - Each area of the continent is currently experiencing a global health disaster caused by Corona Virus (Covid-19). Due to this, there is a critical need to adopt preventive measures to effectively combat this infection. Using a face mask is an effective way to protect everyone in public. When there are large crowds at public places, it is very difficult to monitor a person who is with a mask or without mask using physical checking. Hence developing a solution for detecting face masks is a challenging task. This paper, deals with the implementation of a real time face mask recognition methodology. Using Keras TensorFlow, MobileNetV2 and OpenCv. In this proposed approach, a two-phase system is used to train our model on different datasets from KAGGLE, GITHUB and our own real-world database consisting of people with and without mask. By com-paring our approach with other existing approaches, we find that it has the highest validation accuracy of 99.35%. The performance analysis of presented results also shows that MobileNetV2 is the best approach to detect a face mask with high accuracy.
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