DEVELOPMENT OF A SCALABLE AND REAL-TIME BIOMETRIC-BASED ATTENDANCE MONITORING SYSTEM - Kazeem, Sodiq; Adetunji, Olusogo; Saliu, Abolanle; Oyerinde, Elisha; Salau, Solomon; Ojosipe, Adebisi; Ishola, Pelumi; Adenuga, Nofisat; Uwah, Wisdom; Akinfe, Olayinka (FUDMA Journal of Sciences, 2025)
This research work introduces a scalable and real-time student attendance monitoring system designed to improve accountability and academic discipline through automation and surveillance. Traditional attendance methods like manual roll calls and RFID are often inefficient and prone to manipulation; in contrast, this system uses Face API JS a JavaScript-based deep learning facial recognition library to detect and identify students and lecturers via live video streams captured through webcams. Lecturers are the only authorized personnel to initiate attendance sessions, while students can access their attendance records through a secure web interface. The system incorporates Socket.IO for real-time, bidirectional communication between clients and the server, allowing for seamless updates and efficient attendance coordination. Administrators can monitor live classroom activities using mobile devices or built-in Raspberry Pi modules. Experimental evaluation across varying lighting and classroom conditions yielded an average face recognition accuracy of 95.7%, with a false acceptance rate of 2.1% and average detection latency under 1.2 seconds, confirming the system's robustness and responsiveness. In conclusion, the proposed solution offers an automated, non-intrusive, and transparent attendance tracking mechanism that significantly enhances institutional oversight, reduces human error, and ensures both student presence and lecturer participation.
Keywords: Automation and surveillance; Facial recognition system; Real-time attendance monitoring; Raspberry Pi Integration; Socket IO Communication