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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63280
Volume:
10 |
5 |
May 2026
Pages:
1-4
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 Engineering
SMT. Kashibai Navale College Of Engineering,Pune
Department of Computer Engineering
SMT. Kashibai Navale College Of Engineering,Pune
The proposed system presents an automated attendance management framework using Convolutional Neural Networks (CNN) for accurate and efficient face recognition. The system captures live images through a camera, detects faces, and compares them with trained facial data using deep learning algorithms integrated with OpenCV libraries. It automates subject-wise attendance marking and generates detailed monthly attendance reports, minimizing manual intervention and errors. This approach ensures real-time monitoring, transparency, and accuracy in attendance tracking for institutions and organizations. By leveraging CNN for robust feature extraction and recognition, the system performs effectively under varying lighting and pose conditions, offering a secure, scalable, and intelligent solution for modern attendance management.
KEYWORDS
The Automated Attendance System provides an efficient and reliable alternative to the traditional manual attendance process. By integrating a graphical user interface (GUI) and dataset, the system ensures accuracy, reduces human errors, and saves time. This initial phase of development successfully establishes the foundation for seamless attendance management within institutions and organizations. The current implementation demonstrates how automation can improve productivity and maintain data integrity compared to conventional methods.
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Publication Date
5/15/2026
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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