Automated Facial Recognition Attendance System with Notification System for Educational Institutions

Azegba Okechukwu Levi, Afolabi Idris Yin, Oketa Christian Kelechi, Aniji Ifesinachi Veronica, Eze-Udu Emmanuel

Abstract


This research investigates the methods of attendance in educational institutions in Nigeria, and afterwards developed an Automated facial recognition attendance system for students with a dual notification for both parents and students. This is aimed at addressing the inefficiencies and inaccuracies of traditional attendance management methods by reporting students to their parents when they miss classes and analysing students’ attendance rate for insights. The system leverages cutting-edge artificial intelligence (AI) and computer vision technologies to automate the process of student identification and attendance logging, offering a seamless, reliable, and secure solution for educational institutions. The development process followed the object-oriented analysis and design (OOAD) methodology. It included the creation of a robust system architecture that integrates OpenCV for real-time face detection, the FaceNet model for facial feature extraction, and a MySQL database for efficient data storage and retrieval. The system was built using Python, Flask, and JavaScript, ensuring a responsive and user-friendly web-based interface. To protect sensitive biometric data, AES-256 encryption was implemented, ensuring compliance with modern data security standards. The facial recognition model achieved a 98.4% accuracy rate with an average attendance marking time of just six seconds for a classroom of 30 students. Compared to manual and RFID-based attendance systems, the AI-powered solution demonstrated superior performance in terms of speed, accuracy, and protection against proxy attendance. 


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References


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