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Continuous multibiometric authentication for online exam with machine learning
Citation
Ryu, R and Yeom, S and Kim, S-H, Continuous multibiometric authentication for online exam with machine learning, Proceedings of the 2020 Australasian Conference on Information Systems, 1-4 December 2020, Victoria University of Wellington, New Zealand, pp. 1-7. (2020) [Refereed Conference Paper]
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Copyright Statement
Copyright 2019 authors. This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and ACIS are credited.
Official URL: https://aisel.aisnet.org/acis2020/92/
Abstract
Multibiometric authentication has been received great attention over the past decades with the growing demand of a robust authentication system. Continuous authentication system verifies a user continuously once a person is login in order to prevent intruders from the impersonation. In this study, we propose a continuous multibiometric authentication system for the identification of the person during online exam using two modalities, face recognition and keystrokes. Each modality is separately processed to generate matching scores, and the fusion method is performed at the score level to improve the accuracy. The EigenFace and support vector machine (SVM) approach are applied to the facial recognition and keystrokes dynamic accordingly. The matching score calculated from each modality is combined using the classification by the decision tree with the weighted sum after the score is split into three zones of interest.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | continuous authentication, multibiometric, facial recognition, score-level fusion |
| Research Division: | Information and Computing Sciences |
| Research Group: | Artificial intelligence |
| Research Field: | Artificial intelligence not elsewhere classified |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Artificial intelligence |
| UTAS Author: | Ryu, R (Miss Riseul Ryu) |
| UTAS Author: | Yeom, S (Dr Soonja Yeom) |
| ID Code: | 143519 |
| Year Published: | 2020 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2021-03-22 |
| Last Modified: | 2021-04-07 |
| Downloads: | 12 View Download Statistics |
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