Abstract

To follow the attendance of students is a major concern in many educational institutions. The manual management of the attendance sheets is laborious for crowded classrooms. In this paper, we propose and evaluate a general methodology for the automated student attendance system that can be used in crowded classrooms, in which the session images are taken by a smartphone camera. We release a realistic full-annotated dataset of images of a classroom with around 70 students in 25 sessions, taken during 15 weeks. Ten face recognition algorithms based on learned and handcrafted features are evaluated using a protocol that takes into account the number of face images per subject used in the gallery. In our experiments, the best one has been FaceNet, a method based on deep learning features, achieving around 95% of accuracy with only one enrollment image per subject. We believe that our automated student attendance system based on face recognition can be used to save time for both teacher and students and to prevent fake attendance.