IoT-based Smart Attendance System using Face Recognition and Motion Detection
Umi Syamimi, Lim Chern Hong, Lillian Yee Kiaw Wang · 2024
Managing student attendance is crucial for effective classroom administration. A robust attendance system streamlines the attendance process and ensures accuracy. Many universities and research institutions currently use traditional attendance systems, which have flaws such as data inaccuracy, underutilized attendance records, and vulnerabilities in smart device usage. This research proposes an IoT-based Smart Attendance System using Face Recognition and Motion Detection. The system will employ IoT devices which are the Intel® RealSense™ Depth Camera D455. The integration of facial recognition and motion detection will be explored to model student behaviors in class, including facial recognition, motion detection and time spent on various activities. Additionally, the system aims to assess students’ attention levels and other factors effecting their absenteeism. Data analysis techniques will be applied to identify absenteeism factors such as weather, timetable, subjects, and faculty, facilitating effective communication between users and the system. This ongoing monitoring will link student behavior with academic performance. This research proposed a novel framework in improving learning efficiency and educational processes.