Deep Learning based Approach for Facilitating Online Proctoring using Transfer Learning
J S Ashwinkumar, Harshavardhini Saravana Kumaran, U Sivakarthikeyan, Konjeti P B V Rajesh, R. Vidhya Lavanya · 2021
This paper aims at developing an algorithm which helps to ensure the reliability of online examinations. The proposed algorithm provides an automated approach to facilitate online proctoring which alleviates the cumbersome nature of its manual counterpart. It makes use of transfer learning to realize deep learning and it combines three models namely- YOLO (for fraudulent object and multi-person detection), MPGazeII (for abnormal gaze detection) and VGG16 (for Face recognition) and combines the results of all the individual anomaly detection algorithms to ultimately predict whether the examinee has been engaging in malpractice so that necessary action could be taken. The existing algorithms make use of huge amounts of processing for localization as well as tedious feature extraction to realize online proctoring. The proposed algorithm used Deep Learning to overcome the drawbacks of existing online Proctoring algorithms such that no hand-crafted feature extraction is involved.