Abstract
Cheating during exams is a fraudulent act that sabotages hard-working students’ academic integrity and efforts and damages the standard of education. Traditional ways of invigilation are restricted to physical monitoring of students by humans, and most of the time, invigilators are few in number as compared to students. In this article, we have proposed a computer vision-based deep learning cheating detection model. The proposed deep learning model is based on the highly fast region-based convolution neural network (RCNN) with the MobileNet as the feature extractor. Because of its higher level of examination to detect fraudulent behavior in students, this feature extractor makes it so lightweight that it can process the camera steam with limited computational resources. The model can detect the neck and head movements of the students. The proposed model manages to achieve 92.4% accuracy on neck and head movement detection as compared to the original Faster RCNN and various proposed approaches for detecting these two cheating states.
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Malhotra, M., Chhabra, I. (2023). Student Invigilation System Using Modified Faster RCNN. In: Khanna, A., Gupta, D., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Third Doctoral Symposium on Computational Intelligence . Lecture Notes in Networks and Systems, vol 479. Springer, Singapore. https://doi.org/10.1007/978-981-19-3148-2_63
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DOI: https://doi.org/10.1007/978-981-19-3148-2_63
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