Abstract
Since December 2019, the world has started getting affected by a widely spreading virus which we all call the coronavirus. This virus is spread all across the globe, causing many severe health problems and deaths too. COVID-19 is spread when a healthy person comes in contact with the droplets generated when an infected person coughs or sneezes. So, the WHO has suggested some precautionary measures against the spread of this disease. These measures include wearing a mask in public, maintaining social distancing, avoiding mass gatherings. To help reduce the virus’ spread, in this paper, we are proposing a system that detects unmasked people, identifies them, checks if social distancing is followed or not, and also provides a feature of contact tracing. The proposed system consists of mainly two modules: face mask detection and social distancing. There are two more modules which include face recognition and contact tracing. We used two datasets for training our models. First one to detect masks on faces. For this purpose, we collected the image dataset from GitHub and Kaggle. And, the second dataset was for face recognition in which we took our own images for training purposes. It is hoped that our model contributes toward reducing the spread of this disease. Along with COVID-19, this model can also help reduce the spread of similar communicable disease scenarios.
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Rashinkar, R., Rokade, S., Janjal, P., Pawar, G., Shinde, S. (2022). An Automated System for Facial Mask Detection and Social Distancing during COVID-19 Pandemic. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_3
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DOI: https://doi.org/10.1007/978-981-16-9447-9_3
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