Open AccessFace Mask Detection System
Author(s) -
Yatharth Khansali
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36584
Subject(s) - covid-19 , computer science , face (sociological concept) , pandemic , artificial intelligence , context (archaeology) , face masks , computer vision , transfer of learning , face detection , detector , task (project management) , architecture , facial recognition system , pattern recognition (psychology) , disease , medicine , infectious disease (medical specialty) , engineering , geography , telecommunications , sociology , systems engineering , social science , archaeology , pathology
COVID-19 pandemic has affected the world severely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 176 million people causing over 3.8 million deaths. 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.