Automated Attendance Management System: Leveraging Computer Vision for Efficient Tracking and Monitoring

Face recognition is a bio-metric technology that has been widely adopted for security, authentication, and identification. In recent years, face recognition systems have also been used for attendance management in educational institutions and offices. This paper presents a face recognition-based attendance management system that is designed to replace the time-consuming and error-prone manual attendance process. The system comprises four key phases: database creation, face detection, face recognition, and attendance updating. The initial phase involves the creation of a comprehensive database of images of the students in the class. Subsequently, the system employs the HAAR-Cascade classifier for face detection and the Local Binary Pattern Histogram algorithm for face recognition. The system accurately detects and recognizes faces in real time by analyzing the live video stream from the classroom. Finally, at the conclusion of each session, the attendance records are compiled and stored in a CSV file. The presented system has been validated on a dataset of 100 students. The results show that the system can achieve an accuracy of over 90%. The system is also able to handle variations in lighting and facial expressions. The implementation of this face recognition-based attendance management system can enhance operational efficiency, reduce administrative burdens, and ensure more accurate attendance tracking.