Coronavirus disease is the latest epidemic that forced an international health emergency. It spreads mainly from person to person through airborne transmission. Community transmission has raised the number of cases over the world. Reports indicate that wearing face masks while at work clearly reduces the risk of transmission. Many countries have imposed compulsory face mask policies in public areas as a preventive action. Manual observation of the face mask in crowded places is a tedious task. Thus, researchers have motivated for the automation of face mask detection system. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. A hybrid model using deep and classical machine learning for face mask detection will be presented. The face mask recognition is developed with a machine learning algorithm through the image classification method: MobileNetV2. A face mask detection dataset consists of with mask and without mask images, we are going to use MobileNetV2 to do real-time face detection from a live stream via our webcam. We introduce face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. The steps for building the model are collecting the data, pre-processing, split the data, testing the model, and implement the model. The built model can detect people who are wearing a face mask and not wearing it at an accuracy of 96 percent. This will help track safety violations, promote the use of face masks, and ensure a safe working environment
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