Proctor net: An AI framework for suspicious activity detection in online proctored examinations

Abstract

Due to recent pandemic and other factors, global Education System transiting from traditional paradigm to online paradigm in learning as well as assessment. Online proctored examinations have ensured continuity in assessing learners in award of various global certifications and university degrees. Traditional human proctored examination requires huge infrastructure, human resources, effort, and physical presence of examinees. To overcome these limitations, an automated AI-based proctoring system “Proctor Net” proposed in this work for detecting suspicious behavior of the examinee. The proposed system captures live video of the examinee and generate alerts based on three aspects, 1. Examinee Recognition, 2. Eye-gaze Tracking, and 3. Mouth Opening Detection. In first phase, Proctor Net recognizes examinee faces using inception-Resnet v1 blocks. In next phase, the Proctor Net calculates the pitch and yaw of the authenticated examinee face from the spatial landmarks extracted by hour-glass model. Further, Mouth Aspect Ratio was verified to check if examinee is speaking to others. The proposed model generate alters to proctor if found any deviation in examinee’s behavior. The proposed Proctor Net is evaluated using standard data sets such as Labelled Faces in the wild Dataset (LFW), Unity Eyes Dataset and real time data - “Proctor Dataset” with various types of malpractices. Extensive experimentation is carried out and results demonstrate that proposed work with the combination of Inception-Resnet-v1 blocks with Hourglass modules is more accurate with an accuracy rate of 91%, making it reliable and robust.