Computer Vision in E-Learning: Ensuring Exam Integrity and Lesson Engagement

Abstract

In today's rapidly evolving landscape of online education, maintaining the integrity of examinations and ensuring active student engagement is of utmost importance. Global events like the pandemic have put the spotlight on the urgent need for strong safeguards for online learning platforms. This paper addresses the escalating challenge of academic dishonesty in online exams by proposing an innovative real-time face counting and identity verification system. This research focuses on the development and implementation of a system that leverages Python-based facial recognition tools and cutting-edge computational techniques to accurately detect, count, and verify faces in real-time video streams. By utilizing the OpenCV and face recognition libraries, the system not only ensures that only authorized individuals are present during online exams but also monitors their attention levels, contributing to the enhancement of exam integrity. Through comprehensive testing, this paper demonstrates the system's high accuracy and swift processing, establishing it as a promising solution for real-time monitoring in online examinations and virtual meetings.