Visitor Authentication System Based on Face Recognition Technology

In the era of rapidly advancing security requirements, traditional visitor authentication methods such as ID cards and passwords often fall short in providing robust protection against unauthorized access. This paper presents a real-time visitor authentication system leveraging face recognition technology powered by the YOLOv8 deep learning model. The proposed solution replaces manual verification and RFID-based systems with an automated, contactless, and intelligent approach that captures live facial data through webcam-enabled devices. YOLOv8 ensures high-speed and accurate face detection, while a deep learning-based recognition module matches the detected faces against a dynamically maintained database of registered users. The architecture is designed to support modular deployment across varied security environments such as corporate offices, smart homes, and public infrastructures. Comprehensive testing validates the system's performance, achieving high recognition accuracy and near-instantaneous response times. This work demonstrates the viability of integrating real-time object detection with biometric authentication to enhance security, usability, and scalability in modern access control systems.