Article’s

Face Recognition Attendance Monitoring System offers a cutting-edge replacement for conventional techniques used in educational settings to track attendance. This solution provides a smooth and effective way to track student attendance by utilising the latest developments in artificial intelligence and computer vision. Facial recognition technology replaces manual attendance taking by teachers by automatically recording students’ attendance when they enter classes or other authorised areas. The architecture and implementation of the suggested system, which combines database administration, facial recognition, and detection modules, are described in this work. In order to reliably and precisely track student attendance, the system uses deep learning algorithms to precisely recognise and match faces against a pre-registered database of students. Installing such a system in academic environments has numerous advantages. It gives instructors less administrative work, expedites the attendance management procedure, and offers current information on student attendance trends. By producing thorough attendance reports for administrative needs, it also improves accountability and transparency. The efficacy and efficiency of the Face Recognition Attendance Monitoring System are proven through testing and assessment. The findings show that attendance records are accurately recorded, with few false positives and negatives. It provides an advanced, yet approachable, answer to the problems associated with university attendance tracking. The potential for revolutionising existing attendance monitoring procedures through its adoption lies in its ability to develop a more efficient and data-driven approach to academic oversight and student involvement.