Machine Learning-Driven Facial Recognition for Student Attendance Monitoring

DOI

10.2991/978-94-6463-866-0_50How to use a DOI?

Keywords

Mobile application; Facial recognition; face detection; Machine learning; Deep Learning

Abstract

Tracking student attendance is often a time-consuming task, particularly in large classroom settings, where monitoring each individual's presence becomes challenging. In some instances, students may even impersonate their peers to mark attendance in their absence. To address this issue, the proposed system introduces a mobile-based solution for attendance tracking using machine learning and facial recognition technologies. Leveraging real-time processing and deep learning models—specifically Convolutional Neural Networks (CNNs)—the system identifies and verifies student faces accurately. Designed for mobile platforms, this solution ensures a contactless, efficient, and user-friendly alternative to conventional attendance methods. It significantly reduces the administrative burden on educators, enhances security, and effectively curbs proxy attendance. Performance evaluations highlight the system’s high accuracy and reliability under varying facial expressions, lighting conditions, and camera angles. Testing with a smart attendance dataset revealed accuracy rates of 85% and 92% under different conditions, demonstrating its practicality and innovation as a modern attendance management tool.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Boopalan
AU  - Putti Mohan Krishna
AU  - Pothuru Akhil Chowdary
AU  - Bala Anantha Sai Varshith
AU  - S. Suchitra
PY  - 2025
DA  - 2025/10/31
TI  - Machine Learning-Driven Facial Recognition for Student Attendance Monitoring
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 601
EP  - 612
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_50
DO  - 10.2991/978-94-6463-866-0_50
ID  - Boopalan2025
ER  -