The problem addressed by an online proctoring system lies in the growing need for a reliable and secure method of monitoring and invigilating online exams. With the increasing popularity of remote learning and online education, educational institutions face significant challenges in maintaining the integrity and credibility of assessments conducted in virtual environments. The absence of physical supervision during exams creates an environment where test-takers may be more inclined to engage in dishonest practices, such as cheating or unauthorized collaboration. Ensuring exam integrity becomes a critical concern, as educational institutions strive to provide fair and accurate assessments that truly reflect the knowledge and abilities of the test-takers. Therefore, the problem is to develop an automated online proctoring system that effectively detects and deters cheating behaviors, creating a controlled assessment environment that upholds the validity and reliability of online exams.
Python 3.10.4, Deep learning frameworks (PyTorch, TensorFlow, Keras),OpenCV,Dlib,Flask,HTML5 and CSS3