Facial Recognition-Based Classroom Attendance System with Real-Time Group Photo Processing Using Machine Learning Approach

DOI

10.2991/978-94-6463-858-5_97How to use a DOI?

Keywords

MTCNN; CNN; FaceNet; Face Recognition; Deep Learning; Attendance Tracking; MySQl

Abstract

This paper explores the development and implementation of a face recognition-based attendance system using group photos. The system leverages advanced AI algorithms and deep learning techniques, such as convolutional neural networks (CNNs) and facial landmark detection and Face Recognition, to accurately identify individuals in group photos, ensuring reliable attendance tracking. By storing facial features in a MySQL database, the system ensures fast and secure attendance tracking. The automated process eliminates manual errors, prevents proxy attendance, and significantly reduces time spent on attendance tracking. Unlike biometric fingerprint or RFID systems, which require individual scanning, our system allows for real-time attendance marking with minimal user interaction. Attendance management is a crucial process in educational institutions, affecting academic performance, discipline, and administrative efficiency. Traditional methods, such as manual roll calls, RFID-based systems, and biometric scanners, have several limitations, including human errors, time inefficiency, and vulnerability to proxy attendance. These challenges necessitate a more automated, accurate, and scalable solution. The Face Recognition-Based Attendance System Using Group Photos addresses these issues by leveraging computer vision and deep learning techniques to recognize multiple faces in a single captured image and mark attendance automatically.

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  - Ganesh R. Kadam
AU  - Narendra U. Jadhav
AU  - Md Abdul Wassay
AU  - Yash R. Mohabe
AU  - Kaushal B. Loharkar
AU  - Devendra Singh Kushwaha
PY  - 2025
DA  - 2025/11/04
TI  - Facial Recognition-Based Classroom Attendance System with Real-Time Group Photo Processing Using Machine Learning Approach
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1166
EP  - 1177
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_97
DO  - 10.2991/978-94-6463-858-5_97
ID  - Kadam2025
ER  -