Deep Learning-Based Smart Invigilation System for Enhanced Exam Integrity

This study proposes a smart invigilation system to preserve exam integrity by detecting suspicious student behaviors using deep learning. The model consists of three phases, i.e., student identity verification using face recognition, behavioral sampling for model training utilizing gesture analysis and convolutional 3D networks for emotion analysis, and live video analysis of suspicious activities integrating gesture, emotional analysis, and face recognition. The model is evaluated using 4,000 training and 1,000 test images and identifies non-cheating activities with 99% accuracy and cheating activities with 97.6% accuracy. The proposed model outperforms other methods, achieving accuracies of 98.4% for identifying cheating behaviors and 99.2% for non-cheating behaviors, resulting in an overall accuracy of 98.8% and a low misclassification rate of 1.2%. While the system demonstrates high accuracy, challenges remain in scaling to larger classrooms due to increased computational demand and the need for additional hardware to ensure comprehensive monitoring.