• Prayag Pawar
• Mandar Pawar
• Ruchika Kulve
• Shreya Chavan
• Vivek Waghmare

Coronavirus illness (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus[1].COVID-19 pandemic is continuously advancing all over the world all developing countries have been affected like India, China, the USA, Japan, and Russia. To reduce the transmission of coronavirus in community settings guidelines of CDC, and WHO are followed by people i.e. wearing face masks to protect not only themselves but others. Hence Face detection and recognition will be considered intriguing modalities as detecting certain facial features is a very difficult task since certain parts of the face are hidden. This paper presents a review of various algorithms and different transfer learning approaches used for facemask recognition. This paper describes different types of transfer learning approaches i.e., Haar cascade, Adaboost, VGG-16, Mobilenetv1, and Mobilenetv2are described in this paper. With help of advancing technology and reliable methods which requires less computational power, a good low thermal design power profile and less storage space it can be deployed on IP cameras on an embedded system like raspberry pi and IoT devices. This system has varied applications in community settings which include dense crowded local places. This system will work in public places such as schools, hospitals and bus depots etc. where people need to be monitored with the presence of a facemask and recognize violations and alert local authorities and lower the burden on the healthcare system and law enforcement authorities

— Facial Mask Detection, COVID-19, Deep Learning, Convolutional Neural Network, Internet of things, MobileNetV2

"A SURVEY ON FACEMASK DETECTION USING A CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.g308-g310, May-2022, Available :http://www.jetir.org/papers/JETIR2205728.pdf

"A SURVEY ON FACEMASK DETECTION USING A CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppg308-g310, May-2022, Available at : http://www.jetir.org/papers/JETIR2205728.pdf