COVID-19 Face Mask Live Detection Using OpenCV

Dumala, A.; Papasani, A.; Vikkurty, S.

Severe acute respiratory syndrome coronavirus (SARS-CoV) is recognized, and very first person infected is from the Guangdong province of southern China in 2002 while the virus that causes COVID-19 (Corona VIrus Disease-2019) is known as SARS-CoV-2. World Health Organization (WHO) named it as “COVID-19” on February 11, 2020. Currently, the COVID-19 has frightened the whole world of human beings and pushed into the pandemic. This coronavirus affects the respiratory system by entering into the human body through the droplets of saliva and mucus. It takes 14 days to observe the symptoms of the virus attack. In the meantime, the virus affected person may spread the virus to the coexisting people in the abode unknowingly. Also, it takes 48 h to confirm if a person is virus attacked after the test sample is collected. So, there is a serious need to wear a face mask that covers the nose and mouth besides maintaining the social distance to break the chain of massive increase. This paper attempts to detect if an individual wears a mask, using OpenCV. The accurate identification of landmarks of face in the image is an imperative challenge. Being instinctive it is simple for a human to detect the object, but it took years of research to raise the accessibility of quality datasets and a remarkable progress. The purpose of the paper is identifying the count of faces with the mask in the image and count of faces without a mask on live webcam. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.