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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:8 View Download Statistics

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