Visual system for student attendance monitoring with non-standard situation detection

In this paper we propose a visual system for monitoring of student attendance in seminars and lectures. Basic idea is to estimate the number of people in the room using face detection algorithms and subsequently utilize face recognition algorithms to determine the actual identification of persons (students). Presented approach may be used for multiple purposes. Principal and primary purpose is to monitor attendance, which is possible thanks to university database. When implemented, system is expected to evaluate the attendance automatically or if necessary using collaborative authentication. Non-standard or anomaly detection is another feature that is to be provided by system, subject to tracking are hands, eyes and movement. Proposed solution is expected to improve and facilitate attendance monitoring of students at seminars and lectures. Further it may be used for anomaly prevention (e.g. cheating) and in specific cases for security or legal matters.