Intelligent Class Monitoring System Using Facenet&open Cv

The Intelligent Classroom Monitoring System using FaceNet and OpenCV is an artificial intelligence–driven solution designed to automate attendance tracking and enhance classroom supervision through real-time facial recognition and behavioral analysis. Conventional attendance systems such as manual roll calls, biometric scanners, or RFID-based methods are often inefficient, susceptible to proxy attendance, and limited in monitoring classroom activity. To address these challenges, the proposed system integrates deep learning–based face recognition with computer vision techniques to ensure accurate identification and monitoring of students and faculty members. The system captures live video streams from webcams or IP cameras and processes them using OpenCV for face detection, while FaceNet generates high-dimensional embeddings that uniquely represent each individual. Identity verification is achieved through L2 distance comparison between stored embeddings and detected faces. A randomized attendance capture window is implemented to prevent manipulation and ensure fairness in attendance recording. In addition to attendance automation, the system monitors faculty presence and analyzes classroom behavior through motion detection techniques such as background subtraction and optical flow analysis to identify unusual movement patterns that may indicate classroom disturbances or conflicts. The system architecture includes a web-based dashboard built with modern full-stack technologies that enables administrators to view attendance reports, monitor classroom activities, and receive alerts regarding anomalies. Experimental evaluation indicates that the proposed system achieves recognition accuracy of approximately 92–95% under controlled conditions while maintaining efficient performance on standard hardware. Overall, the system demonstrates the effectiveness of integrating deep learning, computer vision, and web technologies to create a scalable and intelligent classroom monitoring framework that improves institutional management and academic discipline.