Development of a Face Recognition System for Registering Attendance of Students Wearing Mask

In today's given condition a person must execute their daily activities by wearing a mask due to the pandemic. There are certain situations in which the person is forced to remove the mask while performing them. For example, consider the conventional method of the face recognition system for attendance monitoring where the individual is forced to remove the mask which is not appreciable in the current scenario. Hence there is a need to build a system that helps the individual to mark the attendance without the need of removing the mask. It would have been impossible to construct a system that could recognize individuals before the last two decades, but because of advances in the science of computer vision, it is now conceivable. The model uses a Caffe model for face detection and a CNN model for recognition along with the Haar Cascade classifier. The model was created to use real-time applications. The system is built with an accuracy of 90%.