Face Recognition-Based Unauthorized Person Detection And Notification System

Abstract

In today’s world, ensuring secure access to restricted areas is a critical challenge, especially in organizations, research labs, and high-security zones. Traditional access control methods such as ID cards, passwords, or manual monitoring are prone to security breaches, loss, and unauthorized usage. To address these limitations, this project proposes an AI-powered facial recognition system for automated access control integrated with a real-time alert mechanism. The system is based on Artificial Intelligence, specifically leveraging Computer Vision and Machine Learning techniques to accurately detect and recognize human faces. A camera continuously captures live video, from which facial features are extracted and compared against a pre-trained database of authorized individuals. If a match is found, access is granted; otherwise, the system triggers an alert mechanism such as sending notifications or activating alarms. The implementation utilizes tools and technologies including Python, OpenCV for image processing, and deep learning models for face recognition. Optionally, hardware platforms like Raspberry Pi and IoT-based communication modules can be integrated to enhance real-time performance and remote monitoring capabilities. The expected outcome of this project is a reliable, efficient, and contactless access control system that minimizes human intervention and enhances security. The system aims to provide high accuracy in identification, quick response time, and the ability to generate instant alerts in case of unauthorized access, thereby improving overall safety and surveillance in restricted environments.