Mountain Top University Library

In today's fast-paced educational environment, efficient and accurate attendance tracking is crucial for universities. Traditional attendance systems often suffer from shortcomings such as manual recording errors and time-consuming processes. To
address these challenges, this study proposes a face recognition attendance system utilizing OpenCV (Open-Source Computer Vision Library). The system employs stateof-the-art deep learning to automatically detect and recognize faces, enabling seamless
and real-time attendance management. By leveraging the power of computer vision, the proposed system eliminates the need for manual intervention, enhancing accuracy and reducing administrative burdens. The system's implementation involves capturing live
video streams from camera devices, performing face detection and recognition using OpenCV algorithms, and integrating with a centralized database for attendance tracking. Preliminary tests demonstrate promising results in terms of recognition accuracy and
system efficiency. This face recognition attendance system has the potential to significantly streamline attendance processes, improve reliability, and enhance overall productivity within university settings. Keywords: OpenCV algorithms, Face recognition, Attendance system, University