Abstract: Attendance management is an essential requirement in educational institutions and organizations, as it plays a significant role in tracking discipline, participation, and performance. The traditional attendance system, such as manual registers, signature sheets, and roll calls, is time-consuming, prone to errors, and susceptible to proxy attendance. To overcome these drawbacks, this research work proposes a Face Attendance System based on Machine Learning and Computer Vision concepts. The proposed system uses live image acquisition through a camera, face detection through the Haar Cascade algorithm, and face recognition to identify registered users for automatic attendance marking with precise date and time stamping. The proposed system is developed using Python, OpenCV, Flask, and SQLite with a modular approach to distinguish application logic and database management. The experimental results demonstrate that the proposed system greatly minimizes manual labor, increases accuracy, and improves reliability compared to the traditional attendance system. The proposed system can be efficiently employed in educational institutions and organizations as a cost-effective, user-friendly, and scalable solution for automated attendance management.

Keywords: Face Recognition, Attendance System, Machine Learning, Computer Vision, OpenCV, Haar Cascade, Flask, SQLite.

Downloads: | DOI: 10.17148/IARJSET.2026.13239