Home / Current Issue / Paper 1712913
AI-Based Smart Attendance System Using Face Recognition
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: https://doi.org/10.64388/IREV9I6-1712913
Abstract
Attendance management is a critical ad- ministrative task in educational institutions. Traditional attendance systems such as manual registers, RFID cards, and fingerprint biometrics suffer from several limitations including time consumption, proxy attendance, and lack of real-time analysis. This paper presents an AI-based smart attendance system that uses face recognition to automate attendance marking in higher education classrooms. The proposed system captures live video through a webcam, detects faces using Haar-cascade classifiers, and identifies students using 128-dimensional deep face embeddings. Attendance data are securely stored in a local SQLite database and analyzed using an analytics dashboard to visualize attendance trends. In addition, a lightweight rule-based emotion detection mod- ule classifies student expressions into Happy, Neutral, and Tired categories, providing insights into class- room engagement. The experimental evaluation con- ducted on a 50-student dataset achieved a recognition accuracy of 96.7% with an average processing time of approximately one second per frame, demonstrat ing the suitability of the system for real-world deployment.
Keywords
Smart Attendance; Face Recognition; Emotion Detection; Computer Vision; Artificial Intelligence; Student Analytics Smart Attendance; Face Recognition; Emotion Detection; Computer Vision; Artificial Intelligence; Student Analytics
How to cite this paper
@article{1712913,
author = {Mahammad Sinan, Abdul Muneer, Muskan Khanum, Sharika Taskeen, Prof. Dr. Venkatesh T.},
title = {AI-Based Smart Attendance System Using Face Recognition},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {1201-1203},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712913.pdf},
abstract = {Attendance management is a critical ad- ministrative task in educational institutions. Traditional attendance systems such as manual registers, RFID cards, and fingerprint biometrics suffer from several limitations including time consumption, proxy attendance, and lack of real-time analysis. This paper presents an AI-based smart attendance system that uses face recognition to automate attendance marking in higher education classrooms. The proposed system captures live video through a webcam, detects faces using Haar-cascade classifiers, and identifies students using 128-dimensional deep face embeddings. Attendance data are securely stored in a local SQLite database and analyzed using an analytics dashboard to visualize attendance trends. In addition, a lightweight rule-based emotion detection mod- ule classifies student expressions into Happy, Neutral, and Tired categories, providing insights into class- room engagement. The experimental evaluation con- ducted on a 50-student dataset achieved a recognition accuracy of 96.7% with an average processing time of approximately one second per frame, demonstrat ing the suitability of the system for real-world deployment.},
keywords = {Smart Attendance; Face Recognition; Emotion Detection; Computer Vision; Artificial Intelligence; Student Analytics Smart Attendance; Face Recognition; Emotion Detection; Computer Vision; Artificial Intelligence; Student Analytics},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712913}
}