Abstract
All classes are held online in order to ensure safety during the COVID pandemic. Unlike onsite classes, it is difficult for us to determine the full participation of students in the class, as well as to detect strangers entering the classroom. Therefore, We propose a student monitoring system based on facial recognition approaches. Classical models in face recognition are reviewed and tested to select the appropriate model. Specifically, we design the system with models such as MTCNN, FaceNet, and propose measures to identify people in the database. The results show that the system takes an average of 30 s for learning and 2 s for identifying a new face, respectively. Experiments also indicate that the ability to recognize faces achieves high results in normal lighting conditions. Unrecognized cases mostly fall into too dark light conditions. The important point is that the system was less likely to misrecognize objects in most of our tests.
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This research is funded by the University of Science, VNU-HCM, Vietnam under grant number CNTT 2021-13 and Advanced Program in Computer Science.
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Pham, TN., Nguyen, NP., Dinh, NMQ., Le, T. (2022). Tracking Student Attendance in Virtual Classes Based on MTCNN and FaceNet. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_31
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DOI: https://doi.org/10.1007/978-3-031-21967-2_31
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