University Classroom Attendance Based on Deep Learning

Attendance is an important part of classroom evaluation. This paper develops a university classroom automatic attendance system by integrating two deep learning algorithms MTCNN face detection and Center-Face face recognition. A large number of experimental results show that: (1) The system can record such three violations of classroom discipline for automatic attendance, that is absence, lateness and leaving early. An attendance table about all students learning status after class is immediately recorded. (2) The system identifies faces very fast needing only 100 milliseconds to one frame and obtaining a high accuracy. Our face recognition model has an accuracy rate of 98.87% and the true positive rate under 1/1000 the false positive rate is 93.7% on LFW.