Real Time-Based Facial Recognition Driven Attendance System

— Manual attendance management in academic institutions is time-consuming, error-prone, and vulnerable to proxy attendance. To address these limitations, this paper proposes a real-time facial recognition-based group attendance system using Convolutional Neural Networks. The system captures classroom images through a camera, detects multiple faces simultaneously, and identifies students using a trained deep learning model. Recognized faces are automatically mapped to a structured database, and attendance records are updated instantly. The solution is implemented as a Django-based web application that provides subject-wise and day-wise attendance tracking, along with automated graphical analytics. The system enhances accuracy, reduces administrative overhead, and prevents impersonation. Experimental evaluation demonstrates high recognition accuracy and real-time processing capability suitable for classroom environments.