Deep Learning Model for Face Mask Based Attendance System in the Era of the Covid-19 Pandemic
R. Satheesh Kumar, Anagha Rajendran, V Amrutha, G Raghu · 2021
Nowadays we are facing a pandemic, there is a situation where people are not ready to wear face masks, or they do not wear them properly, so, in this research, we are introducing an automatic mask detection system using image processing and soft computing techniques to tackle this problem. In the midst of the pandemic, covering our faces with a mask has become a new normal, as face masks are active in preventing the spread of the virus. Other precautionary measures are also advocated by the government apart from covering faces, to ensure protection and hygiene. In addition, because of the limited supply of masks in the industry, millions of people are learning to make their face masks. On the opposite, identifying faces with masks on any surveillance devices would be demanding while ensuring less access control in buildings. Face coverage with masks is a problem for algorithms and success in face detection. Currently, the authorities have to manually ask people to wear masks even then they tend to fool the authorities, to avoid that we are proposing a face Machine learning-based model of recognition. In the field of computer vision, this is a common research direction by extracting features directly from the detection region and then using machine detection learning algorithms to identify and recognize. In this Face mask detection-based attendance System, people will be only able to mark their attendance only if they wear a mask, besides if they do not wear a mask, they are given an alert and they would have to wear a mask.