Automated attendance system using real-time face recognition

Abstract

Traditional attendance systems in educational institutions and workplaces often rely on manual entry, which can be inefficient, inaccurate, and easily manipulated. To overcome these limitations, this project proposes an automated attendance system that uses real-time face recognition as a reliable and contactless method of identifying individuals. The system is built using Python and OpenCV, employing Haar Cascade classifiers for face detection and the LBPH (Local Binary Pattern Histogram) algorithm for face recognition. A webcam captures live video, and recognized faces are matched against a pre-trained dataset of student images. Once a match is confirmed, the system records the attendance automatically, eliminating the need for manual input. A user-friendly graphical interface, developed using Tkinter, allows users to register new faces, train the recognition model, and view attendance records. The system demonstrates improved accuracy and speed compared to traditional methods, making it suitable for deployment in schools, colleges, and workplaces where efficient attendance management is essential.