An AI-Based Classroom Monitoring System Leveraging Computer Vision and Machine Learning

This paper presents an AI-based classroom monitoring system utilizing computer vision and machine learning. The system employs Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection, FaceNet for facial recognition, and a CNN model trained on the Facial Expression Recognition (FER) 2013 dataset for emotional analysis. A novel heterogeneous approach combines Field Programmable Gate Arrays (FPGA) and a central processor to overcome the limitations of BRAM and complex computation constraints. Evaluation in real-world classrooms yielded a promising 70% accuracy in emotion detection, marking a significant stride in the field. This research not only advances AI-based monitoring systems but also indicates potential applications in surveillance and security.