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Intelligent Offline Exam Monitoring System for Identifying Suspicious Behavior of the Student
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 15 |
| Year of Publication: 2025 |
| Authors: Ganga Holi, Pranamya K.L., Mahima A., Shreya R., Siva Harshitha |
10.5120/ijca2025925125
|
Ganga Holi, Pranamya K.L., Mahima A., Shreya R., Siva Harshitha . Intelligent Offline Exam Monitoring System for Identifying Suspicious Behavior of the Student. International Journal of Computer Applications. 187, 15 ( Jun 2025), 27-33. DOI=10.5120/ijca2025925125
Abstract
In today’s digital education environment, upholding academic integrity during exams is crucial. An intelligent exam monitoring system utilizes advanced image processing algorithms to automatically detect suspicious activity in offline classroom tests. Using Haar cascades and Local Binary Pattern Histograms (LBPH), the system analyzes live video feeds to spot unusual head movements, sideways glances, and other indicators of malpractice. Its modular design facilitates easy integration with surveillance cameras and supports efficient, real-time analysis while minimizing the need for manual proctoring. By enhancing fairness and reducing human intervention, the system provides a robust solution for protecting the integrity of exams. The proposed system develops a complete monitoring system to identify suspicious behavior of the student and achieves 92.3% accuracy.
References
- D. Sırt and E. Saykol, ”COPYNet: Unveiling Suspicious Behaviour in Face-to-Face Exams”, https://doi.org/10.18280/ts.400629 ISSN: 2683-2700,
- T. Liu, ”AI Proctoring for Offline Examinations with 2- Longitudinal-Stream Convolutional Neural Networks”, Computers and Education: Artificial Intelligence, vol. 4, ISSN: 2666-920X, 2023.
- Genemo, ”Suspicious Activity Recognition for Monitoring Cheating in Exams”, in Proc. Indian Natl. Sci. Acad., vol. 88, pp. 1–10, 2022.
- A. Nigam, R. Pasricha, T. Singh, et al., ”A Systematic Review on AI-Based Proctoring Systems: Past, Present, and Future”, Education and Information Technologies, vol. 26, ISSN: 6421–6445, 2021.
- S. Dendir and R. S. Maxwell, ”Cheating in Online Courses: Evidence from Online Proctoring,” ISSN: 2451-9588, vol. 2, 2020.
- S. Sahu and S. Mahapatra, ”AI-Based Proctoring System to Conduct Online Exams: A Study and Future Trends”, ISSN: 101705777, 2020.
- Y. Atoum, L. Chen, A. X. Liu, S. D. H. Hsu, and X. Liu, ”Automated Online Exam Proctoring”, IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1609–1624, 2017.
- K. Hylton, Y. Levy, and L. P. Dringus, ”Utilizing Webcam- Based Proctoring to Deter Misconduct in Online Exams”, Computers and Education, vol. 92–93, ISSN: 0360-1315, 2016.
- M. Reale, S. Canavan, L. Yin, K. Hu, and T. Hung, ”A Multi- Gesture Interaction System Using a 3-D Iris Disk Model for Gaze Estimation and an Active Appearance Model for 3-D Hand Pointing”, IEEE Transactions on Multimedia, vol. 13, no. 3, pp. 474–486, 2011.
- R. Poppe, ”A Survey on Vision-Based Human Action Recognition”, Image and Vision Computing, vol. 28, no. 6, ISSN: 0262-8856, 2010.
- Moyo, Reuben Ndebvu, Stanley Zimba, Michael Mbelwa, Jimmy. (2023). ”A Video-Based Detector for Suspicious Activity in Examination with OpenPose”, 10.48550/arXiv.2307.11413.
- M. T. Jahangir, Numan Subhani, Sadia Nadeem, and Fatima Abid, “AI-Powered Classification for Cheating Detection in Offline Examinations Using Deep Learning Techniques with CUI Dataset,” IJIST, vol. 6, no. 4, pp. 1658–1678, Oct. 2024.
- Jonathan Jobby, ”Elevating Exam Fairness: Advanced Proctoring in Secure Offline Environment”, IJSET, Issue 4, No. 656, ISSN:2395-4752
- Pavan Sharma, Aakash Muthreja, Deeksha Srivastava, Himanshu Bagle, and Kritika, ”Proctoring and Monitoring- Based Examination System”, IJRASET International Journal for Research in Applied Science and Engineering Technology, Paper ID: IJRASET51899, ISSN: 2321-9653.
- Vally, Thatikonda Sofi, Mukhtar, Mudiyala, Manjitha Bakki, Sai, Meghana, and Pattam. (2024). ”Automated Invigilation System for Offline Exam Monitoring Using Deep Learning”, 1-6. 10.1109/ICDSNS62112.2024.10691184
- Dilini, Nimesha Senaratne, Asara Yasarathna, Tharindu Warnajith, Nalin Seneviratne, and Leelanga. (2021), ”Cheating Detection in Browser-Based Online Exams through Eye Gaze Tracking”, 6th International Conference on Information Technology Research (ICITR) 2021, 10.1109/ICITR54349.2021.9657277.
- Ong, Seng Connie, Tee Goh, Michael, (2023). ”Cheating Detection for Online Examination Using a Clustering-Based Approach”, JOIV : International Journal on Informatics Visualization, 7, 2075. 10.30630/joiv.7.3-2.2327.
- Manh Hung, Nguyen Goto, Daisaku. (2024). ”Unmasking academic cheating behavior in the artificial intelligence era: Evidence from Vietnamese undergraduates”, Education and Information Technologies, 29, 15999-16025, 10.1007/s10639-024-12495-4.
- Arumugam, Saravanan. (2025). ”Deep Learning-Based Smart Invigilation System for Enhanced Exam Integrity”, Proceedings of Engineering and Technology Innovation 29: 99-115. 10.46604/peti.2024.14105.
- R. Gupta, D. Saini, and S. Mishra, ”Posture detection using Deep Learning for Time Series Data”, 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2020, pp. 740-744, doi: 10.1109/ICSSIT48917.2020.9214223.
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