Journal
Proceedings of 2022 IEEE Global Conference on Consumer Electronics (GCCE2022)
2022-10
Abstract
Many online handwriting authentications have been developed to prevent proxy-test taking in unsynchronized online exams. Here, we propose an unsupervised anomaly detection method based on principal component analysis calculated from incremental statistics to develop an examinee alternation detection system without template information or pre-training. The evaluation results show that the accuracy of this system is over 90%.
DOI
https://doi.org/10.1109/GCCE56475.2022.10014199
Citation
Taisuke Kawamata, Takako Akakura, “Writer Alternation Detection for Online Exam by Exponential Moving PCA”, pp.156-157, 2022
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