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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64615
Volume:
10 |
6 |
Jun 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
Ashokrao Mane Group Of Institution,Vathar
Department of Computer Science and Engineering
Ashokrao Mane Group Of Institution,Vathar
Department of Computer Science and Engineering
Ashokrao Mane Group Of Institution,Vathar
Department of Computer Science and Engineering
Ashokrao Mane Group Of Institution,Vathar
Department of Computer Science and Engineering
Ashokrao Mane Group Of Institution,Vathar
Department of Computer Science and Engineering
Ashokrao Mane Group Of Institution,Vathar
Intelligent Attendance System is an intelligent and automated classroom attendance solution developed using Artificial Intelligence, Computer Vision, and Face Recognition technologies. The system is designed to overcome the limitations of traditional manual attendance methods, which are time-consuming, less accurate, and prone to proxy attendance and human errors. In this project, live CCTV/mobile cameras installed in classrooms continuously capture video streams, and the AI-based recognition engine automatically detects and recognizes student faces in real time. The system verifies student identity along with classroom details such as year, division, room number, and lecture timetable before marking attendance, thereby improving accuracy and preventing invalid entries. The project supports multiple classrooms simultaneously using multithreading and centralized recognition processing. Technologies such as Python, Flask, OpenCV, DeepFace (FaceNet512), SQLite, HTML, CSS, and JavaScript are used for system development. The system includes features like student management, face data capture, AI face embedding generation, timetable management, live face recognition, automatic attendance marking, real-time CCTV monitoring, multi-camera support, and attendance report generation. By automating attendance management through AI and deep learning techniques, the system reduces manual workload, enhances classroom monitoring efficiency, and demonstrates the practical application of Artificial Intelligence and Smart Surveillance Systems in modern educational institutions.
KEYWORDS
The Intelligent Attendance System System is an innovative and efficient solution that automates the attendance process using Artificial Intelligence, Face Recognition, and Real-Time CCTV Monitoring technologies. The system successfully reduces manual effort, saves valuable lecture time, minimizes human errors, and prevents proxy attendance by accurately identifying students through AI-based face recognition. By integrating timetable management, classroom verification, division-wise filtering, and multi-camera support, the system ensures that attendance is marked only for authorized students in the correct lecture session. The use of technologies such as Python, Flask, OpenCV, DeepFace (FaceNet512), and SQLite/MySQL enables reliable and real- time attendance processing. Furthermore, the system supports multiple classrooms simultaneously through centralized recognition, making it suitable for large educational institutions. Overall, the project demonstrates the practical application of Artificial Intelligence, Computer Vision, and Smart Surveillance Systems in education, providing a secure, accurate, scalable, and intelligent attendance management solution for modern smart classrooms.
[1] OpenCV Development Team, OpenCV: Open Source Computer Vision Library,https://opencv.org [2] Sefik Ilkin Serengil, DeepFace: A Lightweight Face Recognition and Facial Attribute Analysis Framework for Python, Available: https://github.com/serengil/deepface [3] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [4] A. Geitgey, Face Recognition Library Documentation, Available: https://face recognition.readthedocs.io [5] NumPy Developers, NumPy Documentation, Available: https://numpy.org [6]. Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for HumanDetection”(2005) https://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf [7]. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning” (2016) https://www.deeplearningbook.org [8]. OpenCV Documentation – Open Source Computer Vision Library https://opencv.org [9] SciPy Community, SciPy Documentation, Available: https://scipy.org [10]. Gordon Fyodor Lyon, “Nmap Network Scanning” (2009) https://nmap.org/book/man.html
Publication Date
6/8/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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