Abstract
Abstract: The rapid disruption of international trade and travel caused by the COVID-19 epidemic has had an impact on our daily lives. The custom of donning a face mask for protection has evolved. In the near future, a lot of public service providers will demand that clients wear appropriate masks in order to use their services. Identification of face masks is becoming a crucial duty to support world culture. This paper outlines a condensed method for accomplishing this goal utilizing certain fundamental machine learning tools, such as TensorFlow, Keras, OpenCV, and Scikit-Learn. The project's goal is to identify face masks at a public event or gathering. MobileNet V2 is the algorithm employed in the project to accomplish the goal. There is a picture of a few people with and without masks. As an input dataset, a picture of a few individuals wearing and not wearing masks is employed. Pre- processing, data augmentation, training, testing, and image segmentation are some of the steps that go into reaching the project's goal. Creating a model that can identify people who are not wearing masks in public settings is the goal of this project. To guarantee adherence to the standards for public safety, this work can be combined with real-time applications at airports, train stations, businesses,schools, and other public locations.
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