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Remote examination and job interviews have gained popularity and becomeindispensable because of both pandemics and the advantage of remote workingcircumstances. Most companies and academic institutions utilize these systemsfor their recruitment processes and also for online exams. However, one of thecritical problems of the remote examination systems is conducting the exams ina reliable environment. In this work, we present a cheating analysis pipelinefor online interviews and exams. The system only requires a video of thecandidate, which is recorded during the exam. Then cheating detection pipelineis employed to detect another person, electronic device usage, and candidateabsence status. The pipeline consists of face detection, face recognition,object detection, and face tracking algorithms. To evaluate the performance ofthe pipeline we collected a private video dataset. The video dataset includesboth cheating activities and clean videos. Ultimately, our pipeline presents anefficient and fast guideline to detect and analyze cheating activities in anonline interview and exam video.

Cheating Detection Pipeline for Online Interviews and Exams