Smart-Artificial Intelligence Based Online Proctoring System

Abstract

Since COVID 19, there have been significant advancements in the field of teaching and learning. Academic institutions
are going digital to provide their students more resources. Due to technology, students now have more alternatives to study
and improve skills at their own pace. In terms of assessments, there has been a shift toward online tests. The absence of a
physical invigilator is perhaps the most significant impediment in online mode. Henceforth, online proctoring services are
becoming more popular, and AI-powered proctoring solutions are becoming demanding. In this project, we describe a
strategy for avoiding the physical presence of a proctor during the test by developing a multi-modal system. We captured
video using a webcam along active window capture. The face of the test taker is identified and analyzed to forecast his
emotions. To identify his head pose, his feature points are identified. Furthermore, aspects including a phone, a book, or the
presence of another person are detected. This combination of models creates an intelligent rule-based inference system
which is capable of determining if any malpractice took place during the examination.