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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nishchal J, Reddy S, Navya PN (2020) Automated cheating detection in exams using posture and emotion analysis. In: 2020 IEEE international conference on electronics, computing and communication technologies (CONECCT). IEEE, July 2020, pp 1–6
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91–99
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861
Karim IN, Ab Kadir D, Ali F (2020) Development of detection system on suspicious behaviour during exam. J Comput Technol Creative Content (JTec) 5(2):75–81
Khalaf K, El-Kishawi M, Moufti MA, Al Kawas S (2020) Introducing a comprehensive high-stake online exam to final-year dental students during the COVID-19 pandemic and evaluation of its effectiveness. Med Educ Online 25(1):1826861
Liu C, Zhou H, Xu HC, Hou BY, Yao L (2020) Abnormal behavior recognition in an examination based on pose spatio-temporal features. In: 2020 IEEE 5th international conference on cloud computing and big data analytics (ICCCBDA). IEEE, Apr 2020, pp 380–386
Cavalcanti ER, Pires CE, Cavalcanti EP, Pires VF (2012) Detection and evaluation of cheating on college exams using supervised classification. Inf Educ 11(2):169–190
Jalali K, Noorbehbahani F (2017) An automatic method for cheating detection in online exams by processing the student’s webcam images. In: Proceeding 3rd conference on electrical and computer engineering technology (E-Tech), pp 1–6
Arinaldi, A. and Fanany, M.I., 2017, May. Cheating video description based on sequences of gestures. In 2017 5th International Conference on Information and Communication Technology (ICoIC7) (pp. 1–6). IEEE.
Desai N, Pathari K, Raut J, Solavande V (2018) Online surveillance for exam. Int J Recent Trends Eng Res 4(3):331–336
Adil M, Simon R, Khatri SK (2019) Automated invigilation system for detection of suspicious activities during examination. In: 2019 Amity international conference on artificial intelligence (AICAI). IEEE, Feb 2019, pp 361–366
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934
Cote M, Jean F, Albu AB, Capson D (2016) Video summarization for remote invigilation of online exams. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, Mar 2016, pp 1–9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-19-3148-2_63
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3147-5
Online ISBN: 978-981-19-3148-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)