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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63082
Volume:
10 |
5 |
May 2026
Pages:
1-10
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.
Computer Science And Engineering
Priyadarshini Bhagwati College Of Engineering
computer science and engineering
Priyadarshini Bhagwati College Of Engineering
computer science and engineering
Priyadarshini Bhagwati College Of Engineering
computer science and engineering
Priyadarshini Bhagwati College Of Engineering
computer science and engineering
Priyadarshini Bhagwati College Of Engineering
computer science and engineering
Priyadarshini Bhagwati College Of Engineering
This paper presents an AI-driven attendance monitoring system designed to automate and optimize student attendance recording in educational institutions. Traditional roll-call methods are time-consuming, error-prone, and vulnerable to proxy attendance. The proposed system overcomes these limitations by using deep learning and computer vision to recognize multiple student faces from a single classroom photograph with accuracy exceeding 90%. A faculty member captures a group image using a standard mobile camera, after which the system performs preprocessing, face detection, feature extraction, and database matching using OpenCV, dlib, and CNN-based face embeddings. Attendance is automatically marked and exported to an editable Excel sheet for optional human verification. Additionally, real-time absentee notifications are sent to parents through SMS or email. The system includes an analytics dashboard offering insights into attendance trends, detention lists, and student performance patterns. The solution is cost-effective, scalable, hardware-independent, and integrates smoothly with existing ERP/LMS platforms. The results demonstrate that AI-based automation can significantly improve efficiency, accuracy, and transparency in academic administrative processes.
KEYWORDS
The Automated Student Attendance Monitoring and Analysis System provides an efficient and reliable alternative to traditional attendance methods by using AI-based facial recognition to mark attendance from a single classroom image. With accuracy above 90%, the system reduces faculty effort, minimizes errors, and prevents proxy attendance. It works effectively under different lighting conditions, requires no special hardware, and integrates smoothly with existing ERP/LMS platforms. The analytics dashboard further supports data-driven academic decisions. Future improvements may include better low-light accuracy, multi-angle detection, mobile app support, and enhanced privacy protection. Overall, the system demonstrates how AI can modernize academic administration and promote smart, transparent, and eco-friendly educational management.
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Publication Date
5/13/2026
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.
Funding Information
No funding information available for this research.
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