A Deep Learning Approach for Real-Time Face Detection and Automated Notification

Abstract

Face detection and recognition have gained significant attention due to their wide applications in surveillance, access control, and attendance management systems. limited research focuses on application-specific, real-time implementations tailored for school environments with integrated parental notification. This paper proposes an AI-based real-time face recognition and notification system designed to enhance school security and student monitoring. The system uses deep learning-based facial embeddings to accurately detect and recognize students during arrival and departure. Upon successful identification, automated real-time notifications are sent to parents via messaging services, providing immediate updates on their child’s presence. Scalability and reliability are achieved by integrating computational processing with cloud-based infrastructure, enabling efficient real-time performance in practical conditions. Experimental evaluation demonstrates the feasibility and robustness of the proposed framework in improving attendance tracking, strengthening school security, and enhancing parental awareness. Unlike generic face recognition systems, the proposed solution emphasizes a fully automated, application-specific architecture optimized for educational institutions. The study highlights the practical implications of AI-driven monitoring systems in promoting child safety while maintaining data privacy and operational efficiency.