An Automated Attendance System Using Facial Detection and Recognition Technology

Abstract

The proposed system utilizes Haar Cascade algorithms for facial detection and recognition. The system accurately detects and recognizes individuals based on their unique facial features. Facial detection algorithms identify and extract facial regions from input images or video frames, isolating the necessary facial details for further analysis. Subsequently, facial recognition algorithms, LBPH, compare these features with pre-registered faces stored in the system's database, calculating confidence scores to determine individual identities. The system incorporates a user-friendly interface, enabling administrators to easily manage attendance records. They can effortlessly add or remove students from the system's database, access attendance reports, and monitor real-time attendance data.

The proposed Attendance Management System revolutionizes the conventional attendance tracking process by offering enhanced accuracy, efficiency, and security while providing real-time monitoring and comprehensive reporting capabilities. With the potential for adoption in various educational institutions, organizations, and industries, this system represents a significant advancement toward streamlined and intelligent attendance management.