A Robust Approach to Student Attendance Using Web-Based Facial Recognition

Authors

  • Irfan Sahputra Universitas Malikussaleh
  • Muhammad Fikry Universitas Malikussaleh
  • Kurniawati Kurniawati Universitas Malikussaleh

Keywords:

Automated Attendance, Computer Vision, YOLOv8, Face Recognition, Education Technology

Abstract

In this paper, we introduce an innovative student attendance recording system that utilizes computer vision and machine learning to improve attendance management in educational settings. By employing YOLOv8 for real-time face detection and MobileNetV2 for face recognition, the system achieves high accuracy and efficiency across various classroom conditions. Rigorous testing in diverse lighting environments and varying student densities demonstrated a peak recognition accuracy of 98% in well-lit conditions, with an average face detection time of under one second. This system offers a more robust, efficient, and scalable solution than traditional manual attendance methods, addressing common limitations in accuracy and reliability. Future work will target optimization under low-light conditions, enhancing its applicability in real-world scenarios.

References

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Published

2025-01-07

Issue

Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

Section

Articles

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Copyright (c) 2024 Irfan Sahputra, Muhammad Fikry, Kurniawati Kurniawati

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