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Real-Time Face Mask Detection using Deep Learning

Pranad Munjal, Vikas Rattan, Rajat Dua, Varun Malik

Abstract: The outbreak of COVID-19 has taught everyone the importance of face masks in their lives. SARS-COV-2(Severe Acute Respiratory Syndrome) is a communicable virus that is transmitted from a person while speaking, sneezing in the form of respiratory droplets. It spreads by touching an infected surface or by being in contact with an infected person. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. Amid the advancement of technology, deep learning and computer vision have proved to be an effective way in recognition through image processing. This system is a real-time application to detect people if they are wearing a mask or are without a mask. It has been trained with the dataset that contains around 4000 images using 224x224 as width and height of the image and have achieved an accuracy rate of 98%. In this research, this model has been trained and compiled with 2 CNN for differentiating accuracy to choose the best for this type of model.It can be put into action in public areas such as airports, railways, schools, offices, etc. to check if COVID-19 guidelines are being adhered to or not.
Keywords: Respiratory / face masks / deep / differentiating / virus / model / wear / COVID

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