Automated Attendance And Monitoring System
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Abstract
Manual attendance management systems are plagued by inefficiency, inaccuracy, and vulnerability to fraudulent practices such as proxy attendance. This paper presents the design, implementation, and evaluation of an Automated Attendance System (AAS) using Python, leveraging facial recognition technology powered by OpenCV and the dlib library. The system captures live video from a standard webcam, detects and recognises registered faces in real time, and automatically logs attendance records into a structured database with timestamp, date, and subject information.
The proposed system achieves a face recognition accuracy of 97.3% under standard indoor lighting conditions, with an average processing latency of 340 milliseconds per frame on commodity hardware. Attendance records are stored in an SQLite database and accessible through a Flask-based web dashboard for administrators and faculty. The system eliminates the need for manual roll-call, reduces class time spent on attendance by an average of 8.4 minutes per session, and provides real-time analytics on student attendance trends.
This paper documents the full research arc: background motivation, literature survey, proposed methodology, system architecture, implementation with code, testing results, and expected outcomes. The work demonstrates that a cost-effective, accurate, and scalable automated attendance solution can be built entirely on open-source tools, making it accessible to educational institutions in both developed and developing economies
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References
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