Facial Recognition and GPS-Enabled Automated Attendance System

This project has merged GPS geolocation monitoring with facial recognition which emphasizes a system for involuntary attendance that improves attendance management in educational organizations. By including AI-driven facial recognition and geofencing algorithms, the technology ensures real-time authentication, preventing proxy attendance and human error. We have used a Siamese Neural Network-based facial recognition model of student identity, with geolocation validation as the secondary confirmation that a student physically attends class. Such two-layered authentication results in better accuracy, security, and efficiency.This system avoids direct involvement and lessens administrative work, offering real-time monitoring to professors and administrators. Secure data handling and extensive testing ensure reliable and user-friendly performance. This research highlights the potential of education technology in modernizing attendance systems, making academic operations more efficient and secure.