COMPARATIVE ANALYSIS OF FACE RECOGNITION ALGORITHMS FOR AUTOPROCTORING: EIGENFACES, FISHERFACES, CNNS, AND YOLO
Keywords:
Automated Proctoring System, Face Recognition, Image Recognition, Convolutional Neural Networks (CNN), Eigenfaces, Fisherfaces, YOLOAbstract
Auto proctoring, leveraging automated surveillance technologies, has emerged as a solution to monitor online examinations in educational settings. However, its efficacy and ethical implications remain subject to scrutiny. This scientific article presents a thorough investigation into the effectiveness and ethical considerations surrounding auto proctoring systems. Through a review of existing literature and empirical analysis, we aim to provide insights into the benefits, limitations, and ethical challenges associated with the widespread adoption of auto proctoring in educational assessment. Our findings underscore the need for a balanced approach that ensures both academic integrity and student privacy
References
Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037-2041.
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711-720.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778).
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. In Proceedings of the British Machine Vision Conference (BMVC).
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779-788).
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 815-823).
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, Inception-ResNet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1701-1708).
Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.
Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multi-task cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499-1503.
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., & Li, S. Z. (2017). S3FD: Single shot scale-invariant face detector. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 192-201).
