Hossain, Zarin TahiaRoy, ProtyashaNasir, RinaNawsheen, SumaiyaHossain, Muhammad Iqbal2026-08-152026-08-152021-01-01Z. T. Hossain, P. Roy, R. Nasir, S. Nawsheen and M. I. Hossain, "Automated Online Exam Proctoring System Using Computer Vision and Hybrid ML Classifier," 2021 IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON), Dhaka, Bangladesh, 2021, pp. 14-17, doi: 10.1109/RAAICON54709.2021.9929456.97816654786942-s2.0-85142758281https://hdl.handle.net/10361/29093Importance of online education can be seen especially during the ongoing Covid-19 when going to schools or colleges is not possible. So validity of online exams should also be maintained with respect to traditional pen-paper examinations. However, absence of invigilator makes it easy for the examinees to cheat during the exam. Though there are already many systems for online proctoring, not all educational institutes can afford them as the systems are very expensive. In this paper, we have used eye gaze and head pose estimation as the main features to design our online proctoring system. Therefore, the purpose of this paper is to use these features to create an online proctoring system using computer vision and machine learning and stop cheating attempts in exams.14-17en-USfalseCheating predictionHybrid classifierMachine learningMLPOnline proctoringXGBoostComputer vision.Machine learning.Conference Proceeding10.1109/RAAICON54709.2021.9929456