CCTV Integrated Attendance Monitoring System Using Face Recognition
Nishali Dhanshika, Darshan Lahamage, Tanya Anupam, Rajendra R. Sawant · 2024
Traditional attendance tracking in education is often manual and error-prone. Our research introduces an innovative solution, the "CCTV Integrated Attendance Monitoring System Using Face Recognition" wherein we used "live classroom CCTV videos". Leveraging existing CCTV infrastructure, our project offers a cost-effective and technologically advanced attendance monitoring system. The core involves integrating deep learning algorithms, employing ResNet for face detection and FaceNet for recognition. These models are chosen after rigorous comparisons with alternatives like VGGNet and Dlib. The study develops a highly accurate face recognition system using a curated dataset of over 3000 student images. Our system, detailed in architecture, seamlessly integrates ResNet and FaceNet into an efficient backend. The resulting application empowers educators to monitor and manage attendance, updating records in an Excel sheet for administrative convenience. Emphasizing successful student attendance by face recognition, our innovation aims to streamline tracking to reduce administrative workload. By optimizing classroom management, our system contributes to the ongoing evolution of modern educational practices.