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Automated Attendance Monitoring in Buses Using Facial Recognition (Optimized for Moving Environment)
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The increasing demand for intelligent public transport systems calls for automated solutions to manage passenger attendance and ensure safety. This paper presents an Automated Attendance Monitoring System for buses using facial recognition technology, specifically optimized for moving environments. The proposed system captures real-time facial images of passengers using cameras mounted at bus entrances and matches them with pre-stored database images through deep learning-based facial recognition algorithms. Unlike traditional attendance systems that require manual input or RFID cards, this approach ensures non-intrusive, fast, and accurate identification even under dynamic lighting and motion conditions. The paper focuses on techniques such as motion compensation, frame stabilization, and face tracking to enhance detection accuracy while the bus is in motion. Experimental results show that the system achieves an accuracy of over 92% in varying environments, making it a reliable and scalable solution for smart transportation networks.
Keywords
Facial Recognition, Automated Attendance, Moving Environment, Smart Bus System, Deep Learning.
References
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[3] ” Face Detection using LBP Features”Jo Chang-yeon CS 229 Final Project Report December 12 ,2008.
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[5] Himanshu Tiwari1“Live Attendance System via Face Recognition” International Journal for Research in Applied Science & Engineering Technology (IJRASET) Volume 6 Issue IV, April 2018.
[6] “FACE RECOGNITION TECHNIQUE USING ICA AND LBPH”, Khusbu Rani1, Sukhbir , kamboj2,IRJET,Volume-04,issu08,August-2017
[7] E. Varadharajan, R. Dharani, S. Jeevitha, B. Kavinmathi, S. Hemalatha, Automatic attendance management system using face detection, Online International Conference on Green Engineering and Technologies (IC-GET), 978-1-5090-4556-3/16/$31.00 ©2016 IEEE.
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[10] Smart Attendance Management System Using Face Recognition Kaneez Laila Bhatti1, *, Laraib Mughal1, EAI Endorsed Transactions on Creative Technologies 10 2018 | Volume 5 | Issue 17 | e4.
How to cite this paper
@article{1712220,
author = {Priyadharshini P, Nainthiga T, Niranjana R, Maanisha S},
title = {Automated Attendance Monitoring in Buses Using Facial Recognition (Optimized for Moving Environment)},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {1452-1455},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712220.pdf},
abstract = {The increasing demand for intelligent public transport systems calls for automated solutions to manage passenger attendance and ensure safety. This paper presents an Automated Attendance Monitoring System for buses using facial recognition technology, specifically optimized for moving environments. The proposed system captures real-time facial images of passengers using cameras mounted at bus entrances and matches them with pre-stored database images through deep learning-based facial recognition algorithms. Unlike traditional attendance systems that require manual input or RFID cards, this approach ensures non-intrusive, fast, and accurate identification even under dynamic lighting and motion conditions. The paper focuses on techniques such as motion compensation, frame stabilization, and face tracking to enhance detection accuracy while the bus is in motion. Experimental results show that the system achieves an accuracy of over 92% in varying environments, making it a reliable and scalable solution for smart transportation networks.},
keywords = {Facial Recognition, Automated Attendance, Moving Environment, Smart Bus System, Deep Learning.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712220}
}