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Automated ai-based proctoring for online testing in e-learning system

НазваAutomated ai-based proctoring for online testing in e-learning system
Назва англійськоюAutomated ai-based proctoring for online testing in e-learning system
АвториOleh Shkodzinsky, Mykhailo Lutskiv
ПринадлежністьTernopil Ivan Puluj National Technical University, Ternopil, Ukraine
Бібліографічний описAutomated ai-based proctoring for online testing in e-learning system / Oleh Shkodzinsky, Mykhailo Lutskiv // Scientific Journal of TNTU. — Tern.: TNTU, 2022. — Vol 107. — No 3. — P. 76–85.
Bibliographic description:Shkodzinsky O., Lutskiv M. (2022) Automated ai-based proctoring for online testing in e-learning system. Scientific Journal of TNTU (Tern.), vol 107, no 3, pp. 76–85.
DOI: https://doi.org/10.33108/visnyk_tntu2022.03.076
УДК

378.14

Ключові слова

face recognition, photo fixation, knowledge testing, image recognition algorithms, person identification, identification accuracy.

Based on the analysis of existing on the market algorithmic solutions for identity verification during knowledge control in electronic learning systems, the requirements for the target system were formed. The main algorithms and approaches to the detection and recognition of faces were considered, as a result of which an effective combination of algorithms was chosen. The system of photo fixation and identity verification during knowledge control in LMS ATutor was designed and implemented. Its effectiveness was verified on the basis of a sample of test passes during its work in the real conditions of the educational process. Conclusions were made regarding the feasibility of implementation.

ISSN:2522-4433
Перелік літератури
  1. Shkodzinsky O. K., Lutskiv M. M., Smolii I.-M. S. Rozvytok zasobiv veryfikatsii osoby ta yii dii pry kontroli znan v umovakh dystantsiinoho navchannia. Zbirnyk tez dopovidei Ⅹ Mizhnarodnoi naukovo-praktychnoi konferentsii molodykh uchenykh ta studentiv „Aktualni zadachi suchasnykh tekhnolohii“,
    24–25.11.2021. T.: FOP Palianytsia V. A., 2021. Vol I. P. 138–139. (Kompiuterno-informatsiini tekhnolohii ta systemy zviazku). [In Ukrainian].
  2. Best Online Proctoring Software, 2020. G2 Bussness Software Reviews. URL: https://www.g2.com/ categories/online-proctoring.
  3. Yang M. H., Kriegman D. J., and Ahuja N. 2002. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (1): 34–58. ISSN 01628828. doi:10.1109/34.982883.
  4. Lam, K.-M. and Yan, H., 1994. Fast algorithm for locating head boundaries. Journal of Electronic Imaging, 3 (4): 351–359. ISSN 1017-9909. Doi:10.1117/12.183806.
  5. Chi L., Zhang H. and Chen M., 2017. End-To-End Face Detection and Recognition. arXiv preprint, 1703.10818:1-9.
  6. Moghaddam B. and Pentland A. 1997. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7): 696–710. ISSN 01628828. doi:10.1109/34.598227.
  7. Ou W., You X., Tao D., Zhang P., Tang Y., and Zhu Z., 2014. Robust face recognition via occlusion dictionary learning. Pattern Recognition. 47 (4): 1559–1572. ISSN 00313203. Doi: 10.1016/j.patcog.2013.10.017.
  8. Gao Y. and Qi Y., 2005. Robust visual similarity retrieval in single model face databases. Pattern Recognition, 38 (7): 1009–1020. ISSN 00313203. doi:10.1016/j.patcog.2004.12.006.
  9. Essa I. and Pentland A., 2002. Facial expression recognition using a dynamic model and motion energy. In Proceedings of 5th IEEE International Conference on Computer Vision. 20–23 June 1995.P. 360–367. IEEE. Doi: 10.1109/ iccv.1995.466916.
  10. dlib default face detector – dlib documentation. URL: http://dlib.net/python/#dlib.get_frontal_face_detector.
  11. King Davis E. 2015 Max-margin object detection. arXiv preprint arXiv:1502.00046.
  12. GitHub – davisking/dlib-models: Trained model files for dlib example programs. URL: https://github.com/ davisking/dlib-models#dlib_face_recognition_resnet_model_v1datbz2.
  13. LFW Face Database. URL: http://vis-www.cs.umass.edu/lfw/.
  14. ImageNet Winning CNN Architectures (ILSVRC). URL: https://www.kaggle.com/getting-started/149448.
  15. Agrawal S. and Khatri P. Facial Expression Detection Techniques: Based on Viola and Jones Algorithm and Principal Component Analysis, 2015 Fifth International Conference on Advanced Computing & Communication Technologies. 2015. P. 108–112. Doi: 10.1109/ACCT.2015.32.
     

 

References:
  1. Shkodzinsky O. K., Lutskiv M. M., Smolii I.-M. S. Rozvytok zasobiv veryfikatsii osoby ta yii dii pry kontroli znan v umovakh dystantsiinoho navchannia. Zbirnyk tez dopovidei Ⅹ Mizhnarodnoi naukovo-praktychnoi konferentsii molodykh uchenykh ta studentiv „Aktualni zadachi suchasnykh tekhnolohii“,
    24–25.11.2021. T.: FOP Palianytsia V. A., 2021. Vol I. P. 138–139. (Kompiuterno-informatsiini tekhnolohii ta systemy zviazku). [In Ukrainian].
  2. Best Online Proctoring Software, 2020. G2 Bussness Software Reviews. URL: https://www.g2.com/ categories/online-proctoring.
  3. Yang M. H., Kriegman D. J., and Ahuja N. 2002. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (1): 34–58. ISSN 01628828. doi:10.1109/34.982883.
  4. Lam, K.-M. and Yan, H., 1994. Fast algorithm for locating head boundaries. Journal of Electronic Imaging, 3 (4): 351–359. ISSN 1017-9909. Doi:10.1117/12.183806.
  5. Chi L., Zhang H. and Chen M., 2017. End-To-End Face Detection and Recognition. arXiv preprint, 1703.10818:1-9.
  6. Moghaddam B. and Pentland A. 1997. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7): 696–710. ISSN 01628828. doi:10.1109/34.598227.
  7. Ou W., You X., Tao D., Zhang P., Tang Y., and Zhu Z., 2014. Robust face recognition via occlusion dictionary learning. Pattern Recognition. 47 (4): 1559–1572. ISSN 00313203. Doi: 10.1016/j.patcog.2013.10.017.
  8. Gao Y. and Qi Y., 2005. Robust visual similarity retrieval in single model face databases. Pattern Recognition, 38 (7): 1009–1020. ISSN 00313203. doi:10.1016/j.patcog.2004.12.006.
  9. Essa I. and Pentland A., 2002. Facial expression recognition using a dynamic model and motion energy. In Proceedings of 5th IEEE International Conference on Computer Vision. 20–23 June 1995.P. 360–367. IEEE. Doi: 10.1109/ iccv.1995.466916.
  10. dlib default face detector – dlib documentation. URL: http://dlib.net/python/#dlib.get_frontal_face_detector.
  11. King Davis E. 2015 Max-margin object detection. arXiv preprint arXiv:1502.00046.
  12. GitHub – davisking/dlib-models: Trained model files for dlib example programs. URL: https://github.com/ davisking/dlib-models#dlib_face_recognition_resnet_model_v1datbz2.
  13. LFW Face Database. URL: http://vis-www.cs.umass.edu/lfw/.
  14. ImageNet Winning CNN Architectures (ILSVRC). URL: https://www.kaggle.com/getting-started/149448.
  15. Agrawal S. and Khatri P. Facial Expression Detection Techniques: Based on Viola and Jones Algorithm and Principal Component Analysis, 2015 Fifth International Conference on Advanced Computing & Communication Technologies. 2015. P. 108–112. Doi: 10.1109/ACCT.2015.32.
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