Event Shield Security: A Secure Event Management System Using Facial Recognition and Real-Time Crowd Monitoring

Abstract Event management systems are vital for ensuring smooth operations and maintaining safety during large gatherings. Yet, many of the solutions in use today still rely on manual registration, QR code ticketing, and conventional surveillance methods. These approaches often lead to inefficiencies such as ticket duplication, long entry delays, overcrowding, and the absence of effective real-time monitoring. To overcome these challenges, this paper introduces Event Shield, an intelligent event management system that integrates facial recognition for seamless registration and authentication with automated crowd monitoring for enhanced safety. By removing the need for physical tickets, the system minimizes the risk of fraudulent entry and streamlines access. An administrator dashboard further supports real-time oversight by enabling user management, attendance tracking, and instant alerts when unusual activities or crowd density thresholds are detected. The system has been implemented using Python, OpenCV, and Tkinter, with CSV-based data handling for storing user details and facial templates. Preliminary testing shows that Event Shield provides faster check-in compared to QR verification, while achieving higher accuracy and reducing insider threats. Overall, the proposed solution offers a practical and scalable approach that bridges the gap between traditional event workflows and modern security needs, making it well-suited for large-scale events, smart campuses, and public safety applications. Keywords: Event management, Facial recognition, Crowd monitoring, Security systems, Automated authentication, Real-time alerts.