Student, Department of Applied Physics, Delhi Technological University, New Delhi, India.
Research Article
World Journal of Advanced Research and Reviews, 2022, 13(02), 232-238
Article DOI: 10.30574/wjarr.2022.13.2.0142
Received on 07 January 2022; revised on 09 February 2022; accepted on 11 February 2022
After the rise of the coronavirus, every country has made a compulsion on wearing face masks in public places. Even though a few countries have reached a good number of vaccinations to date, the human body is still not immune to the new variants of the virus. It will take a few more years till everyone becomes mask-free. Thus, authorities need a system to keep a proper check on the discipline whether everyone is wearing a mask in a public place or not. This paper proposes a model with 99.5% accuracy which can be deployed and thus can monitor public places. This will help the authorities to control the spread of the virus. The model proposed is a fast model which took only 38.03 seconds on average per epoch while training. Using the Kaggle dataset, the CNFMD model is trained and tested. The dataset, pre-split into three parts is used from training, validation as well as testing of the model.
Machine Learning; Deep Learning; COVID-19; Face Mask
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Saksham Checker. CNFMD: Convolutional Network-based Face Mask Detection. World Journal of Advanced Research and Reviews, 2022, 13(2), 232-238. Article DOI: https://doi.org/10.30574/wjarr.2022.13.2.0142