Real Time E-learning Students Monitoring for Optimization Facial Landmark Recognition Based on Hybrid Deep Learning Techniques

The onset of digital education, propelled by the global COVID-19 crisis, has revolutionized the education sector, presenting unique difficulties, including the crucial task of preserving academic honesty. This study explores the possibilities of computer vision technologies, specifically face recognition and detection systems, to deter dishonest practices in online learning contexts. In this article we aim to construct efficacious strategies that leverage these technologies to track student actions in real-time and alert educators about possible cheating instances. This study presents two innovative models addressing cheating in online learning settings using cutting-edge computer vision techniques. Our initial model is an ensemble learning based face recognition system that blends the functionalities of three different deep learning (DL) structures: VGG, MobileNet, and DenseNet. This ensemble learning approach aims to offset the shortcomings of individual models while amplifying the overall effectiveness. The model’s efficiency will be gauged by juxtaposing it with other models and testing its performance against renowned benchmark datasets. Following this, we propose a second mod