Realtime Monitoring Using Face Recognition

This project presents a real-time student monitoring system using deep learning for accurate and efficient face recognition in educational environments. It integrates MTCNN for face detection, FaceNet for feature extraction, and SVM for lightweight classification. The system tracks student movements across campus locations like classrooms, labs, and restrooms, providing real-time updates to administrators and enhancing security and accountability. Designed for low-resource devices, it operates efficiently under varied lighting and environmental conditions. Future enhancements include scaling for larger populations, improving real-time responsiveness, and deploying on edge devices for cost-effective scalability. This solution promotes smarter, safer, and more organized educational spaces through AI-driven monitoring.