AI-Powered Security and Attendance Management System Using Deep Learning and Facial Recognition

Abstract

This project presents a comprehensive AI-driven security and attendance management system, designed to leverage state-of-the-art deep learning and facial recognition technologies. By employing Convolutional Neural Networks (CNNs) and the YOLO v8 algorithm, the system achieves precise, real-time classification of individuals as either authorized or unauthorized when they attempt to access buildings or classrooms. The framework utilizes advanced facial feature mapping and high-speed image analysis, ensuring robust identity verification through seamless comparisons with a secure, pre-registered database. In academic environments, the system automates the process of attendance tracking with high accuracy, dynamically logging the presence of students and significantly reducing administrative workloads. It enhances the security infrastructure by integrating automated alert mechanisms; upon detecting unauthorized entry, the system sends instant notifications and issues security warnings to designated personnel, activating proactive threat mitigation protocols. The intelligent response suite includes real-time SMS and email alerts to ensure immediate action is taken. This innovative solution combines the efficiency of automated attendance systems with advanced security protocols, setting a new benchmark in smart building management. By integrating AI-driven facial recognition and automated response systems, it enhances operational efficiency, ensures reliable access control, and promotes a safer, more secure environment for academic and institutional use.