Abstract
The function of proctors is to detect academic improprieties during examinations, tests, and laboratory experiments. With the breakout of the pandemic, many institutions went to online operation, which rendered a difficulty to the exams and tests. Then, various remote proctoring methods are employed. Virtual proctor (VP) systems that replace continuous human supervision with video surveillance are starting to become popular in distance education, despite their most common shortcomings, which include their vulnerability to pose translations of the learners and their de-pendency on the illumination conditions of the environment. As a result, virtual proctor systems impose extra constraints on the movements of the learners being monitored, and thus they may feel uncomfortable when being subjected to virtual proctoring. At present, in the design of VP systems, sufficient consideration of human factors and ergonomics is typically not given.
To overcome these shortcomings, a video-based VP system with a reliable two-stage face recognition and tracking method is proposed here. First, the face region is detected and cropped out from the video frames by a combination of eye, mouth, and face detection. After that, to render the usage of the VP system more comfortable for the learners being monitored, a modified face recognition method based on a proposed improved real-time stereo-matching algorithm is employed to track the learners’ movements. The VP system is capable of identifying a limited set of pre-defined suspicious behaviors that may represent cheating. In order to evaluate the efficiency of the proposed methods, two benchmark analyses with respect to the effect of pose translations and varying illumination conditions on the effectiveness of the VP system are presented. It is asserted that the proposed VP system is simpler to use than prior solutions and realizes an ergonomic design that makes the learners monitored more comfortable.
