Attendance Tracking with Perception Detection using Recurrent Neural Network


  • Glodelyn D. Ocfemia, Karren V. De Lara


Online class, (RNN)Recurrent Neural Network, Attendance, body motion, face expression,(LSTM) Long-Short Term Memory


The online class environment has become prevalent in education since the Covid-19 pandemic. It offers students and educators the flexibility to access online education from various locations. Despite its challenges, the online class environment has become essential for providing accessible education, especially during the times of disruptions. There are still educators that conduct attendance checking manually so the student’s attendance and attentiveness during online classes is a big challenge for teachers. The researcher developed a system that can detect body motion and face expression in an online class setup. Attendance tracking with perception detection with the use of Recurrent Neural Network is suggested to detect body motion and face expression, capture and then, store the students’ attendance in the system. The system will use real-time detecting the body motion and face expression whether the students is attentive or not are being captured to store the student attendance during online classes. The body motion will detect the body and the facial expression to capture and track when the camera is on. The system incorporates a notification within the system for faculty regarding inattentive students. Also, the system developed as a separate web application that is compatible or complementary with the existing setup of online classes.


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How to Cite

Karren V. De Lara, G. D. O. . (2024). Attendance Tracking with Perception Detection using Recurrent Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1261–1266. Retrieved from



Research Article