Leveraging Facial Recognition for Automated Attendance Tracking

Student: Simon Tamunosaki Telema (Thesis, 2025)
Department of Computer Science and Information Technology
Crawford University of Apostolic Faith Mission Faith City, Igbesa, Ogun State

Abstract

This project explores the development of an automated attendance tracking system using facial recognition technology. Traditional attendance methods—such as manual sign-ins and RFID cards—are often prone to inaccuracies, time consumption, and fraudulent practices like proxy attendance. The proposed system introduces a contactless and secure approach by leveraging computer vision and facial recognition to accurately identify individuals and mark attendance in real time. The system was implemented using web technologies including HTML, CSS, JavaScript, PHP, and MySQL, with OpenCV used for facial recognition. It includes features such as live face scanning, geo-fencing for location verification, real-time reporting, and a user-friendly interface for students, teachers, and administrators. Evaluation showed the system achieved over 90% accuracy in controlled environments, significantly improving efficiency and minimizing administrative workload. This work contributes to the modernization of attendance systems in educational and organizational settings, offering a scalable, secure, and efficient alternative to conventional methods.

Keywords
leveraging facial recognition automated attendance tracking
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