AUTOMATED STUDENT ATTENDANCE MONITORING AND ANALYTICS SYSTEM FOR COLLEGES

Abstract

This paper presents an Automated Student Attendance Monitoring and Analytics System for Colleges that integrates Artificial Intelligence based face recognition with modern web technologies to provide a centralized, web-based platform for recording, monitoring, and analyzing student attendance. The system enables faculty to mark attendance automatically through real-time face detection and recognition, while students and administrators access attendance history, subjectwise percentages, and institutional analytics through role-based dashboards. The platform further integrates classroom management, exam-eligibility verification, online proctoring, and automated Excel/PDF report generation within a single application built using Flask (Python), HTML/CSS/JavaScript, SQLite, and OpenCV-based ONNX recognition models. The proposed system addresses the limitations of traditional manual, register-based attendance methods by eliminating proxy attendance, reducing administrative workload, and enabling real-time, data-driven academic decision-making.in this many uses there with the project developed by the computer vision and more security options.