Research On Facemask Detection for Preventing Covid Spread Using Transfer Learning Based Deep Neural Network

The COVID-19 pandemic disrupted people’s livelihoods and hindered global trade and transportation. During the COVID-19 pandemic, the World Health Organization mandated that masks be worn to protect against this deadly virus. Protecting one’s face with a mask has become the standard. Wearing a mask is an important way of preventing COVID-19 transmission and infection. However, people do not like to cover their face with a proper face mask and some of them do not know how to wear it properly. Checking it manually for a large group of people, especially at a crowded place like a train station, theatre, classroom or an airport, can be time consuming and expensive. Also, people can be biassed and gullible. Therefore, an automated, accurate and reliable system is required for the task. To train the system adequately, lots of data is required: images. The system should recognize if a person is not wearing a face mask at all, wearing it improperly or if the one is wearing it properly. The paper focuses on developing a Python-based image processing system utilising transfer learning with deep neural networks to enhance COVID-19 prevention measures. It involves face detection, localization, and mask detection. In the absence of a mask, the system identifies the person and notifies the relevant authorities via email.