Facial Recognition-Based Attendance Management

Abstract

This study presents an automated attendance system using face recognition and deep learning, improving efficiency by detecting students in real time and reducing manual tracking. Optimizations like multi-threading and frame skipping enhance speed and accuracy, supporting up to 10 faces simultaneously. Attendance records are stored in CSV/Excel, while a PyQt-based GUI ensures userfriendly interaction for student registration, automated tracking, and administrator control. The system reduces errors, increases reliability, and can be adapted for educational institutions, workplaces, and security systems.