This system revolutionizes the traditional attendance process by leveraging facial recognition technology for seamless and automated attendance tracking. Designed to identify registered users in real-time through a webcam, the system uses OpenCV for efficient face detection and the K-Nearest Neighbors (KNN) algorithm for accurate identification.
• Real-Time Identification: The system captures live video feed and identifies users in real-time, ensuring swift and accurate attendance logging.
• Facial Recognition: Advanced face detection algorithms ensure reliability and precision, even in dynamic environments.
• Data Management: Attendance records are systematically stored in CSV files for easy access and analysis.
• User-Friendly Interface: Built using Flask, the web application provides an intuitive interface for managing user data, attendance reports, and system settings.
• Scalability and Security: The architecture ensures scalability to accommodate a large user base while maintaining data integrity and security.
This project demonstrates the practical application of AI and machine learning techniques in everyday processes, offering a robust, contactless, and time-efficient attendance solution.