A vision-based behavioural monitoring framework towards trustworthy AI proctoring for online assessments

This research outlines a vision-based framework for precise head pose estimation, integrating face detection, facial landmark detection, and 3D pose estimation. The methodology demonstrates high accuracy across its components, making it suitable for applications requiring robust human behavioral monitoring, such as human-computer interaction, driver monitoring, and security surveillance.

Accurate and robust head pose estimation is critical for advancing technologies that rely on understanding human attention and behavior. This framework provides a foundational capability for developing more sophisticated and reliable monitoring systems across diverse sectors, enhancing safety, interaction quality, and surveillance efficacy.

A face detection model achieved 94% accuracy with high precision, recall, and F1-score.

Forward consideration, not a verified fact.