Abstract
Abstract: COVID-19 pandemic has affected the world gravely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 170 million people causing over 3.6 million deaths [1] . Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don’t wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.
Published Version
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