Attendance Management System Using Face Recognition

Abstract

Face recognition technology has become a vital tool in image processing and pattern recognition, offering innovative solutions for applications such as attendance management systems. This project introduces a face recognition-based attendance system designed to overcome the limitations of traditional manual and biometric methods, such as proxy attendance, time consumption, and hygiene concerns. By utilizing the Haar-Cascade algorithm, the system is capable of accurately detecting and recognizing human faces even under challenging conditions involving variations in lighting, facial expressions, and head poses. The overall process includes five key stages: image capture, face detection, facial comparison, recognition, and updating the attendance database. During implementation, the system captures live images of students, detects their faces, compares them with stored data, and upon successful recognition, marks their attendance automatically. This contactless and automated approach not only enhances the security and accuracy of attendance tracking but also modernizes the administrative process in educational institutions. Rigorous testing has shown that the system performs reliably with a high recognition rate, making it a practical solution for preventing proxy attendance and ensuring a seamless classroom management experience.