A Real-Time Automated Attendance System Using Face Recognition

Abstract

This research presents a fully automated, real-time attendance system that integrates YOLOv8 and FaceNet architectures to overcome the limitations of manual and RFID-based methods. Traditional attendance tracking is often inefficient and prone to proxy attendance; the proposed solution addresses these issues by employing a contactless, high-precision biometric approach. The system pipeline utilizes the YOLOv8n-face model for rapid face detection from live video feeds, capable of identifying multiple individuals simultaneously even in crowded environments. Detected faces are processed by FaceNet to generate 128-dimensional embeddings, which serve as unique digital signatures for identity verification. This integration ensures a hygienic, seamless user experience suitable for educational and corporate institutions in a post-pandemic context. Performance evaluations demonstrate the system’s robustness, achieving a detection accuracy of 98.7% and a recognition accuracy of 95.2% while maintaining a real-time processing speed of 30 frames per second on standard hardware. The system logs attendance data in CSV format and includes an SMS notification feature to enhance administrative transparency. Despite its success, the study identifies challenges regarding variable lighting conditions and facial occlusions, such as masks. Future work is directed toward implementing advanced lighting normalization techniques and privacy-preserving encryption protocols to further secure sensitive biometric data and ensure compliance with data protection regulations.