Review on Literature Survey of Human Recognition with Face Mask

The COVID-19 is an unparalleled crisis leading to a huge number of casualties and security problems. To reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of the researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. This paper aims to present a review of various methods and algorithms used for human recognition with a face mask. Different approaches i.e. Haar cascade, Adaboost, VGG-16 CNN Model, etc. are described in this paper. A comparative analysis is made on these methods to conclude which approach is feasible. With the advancement of technology and time more reliable methods for human recognition with a face mask can be implemented in the future. Finally, it includes some of the applications of face detection. This system has various applications at public places, schools, etc. where people need to be detected with the presence of a face mask and recognize them and help society.