Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Automated Student Attendance Monitoring and Analysis System for Colleges.

Author(s) Prof. Ashish P Mohad, Mr. Yash S Bajpai, Ms. Diksha N Kore, Mr. Siddesh P Lambade, Mr. Rishikumar H Sahu, Mr. Vivek G Sharma
Country India
Abstract 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, realtime 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 Facial Recognition, Attendance Automation, Deep Learning, Computer Vision, Educational Technology.
Field Engineering
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-30
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E-ISSN 3048-7641
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