A Novel Deep Learning-based Online Proctoring System using Face Recognition, Eye Blinking, and Object Detection Techniques

Abstract

Distance and online learning (or e-learning) has become a norm in training and education due to a variety of benefits such as efficiency, flexibility, affordability, and usability. Moreover, the COVID-19 pandemic has made online learning the only option due to its physical isolation requirements. However, monitoring of attendees and students during classes, particularly during exams, is a major challenge for online systems due to the lack of physical presence. There is a need to develop methods and technologies that provide robust instruments to detect unfair, unethical, and illegal behaviour during classes and exams. We propose in this paper a novel online proctoring system that uses deep learning to continually proctor physical places without the need for a physical proctor. The system employs biometric approaches such as face recognition using the HOG (Histogram of Oriented Gradients) face detector and the OpenCV face recognition algorithm. Also, the system incorporates eye blinking detection to detect stationary pictures. Moreover, to enforce fairness during exams, the system is able to detect gadgets including mobile phones, laptops, iPads, and books. The system is implemented as a software system and evaluated using the FDDB and LFW datasets. We achieved up to 97% and 99.3% accuracies for face detection and face recognition, respectively.