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International Conference on Computer Recognition Systems

CORES 2017: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 pp 221–228Cite as

Projection-Based Person Identification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

Abstract

The increasing interest in person identification based on keystroke dynamics can be attributed to several factors. First of all, it is a cheap and widely applicable technique, whereas online services such as internet banking or online tax declaration require reliable person identification methods. Furthermore, there are various attack techniques against the existing identification methods, thus combining the existing methods with new person identification methods could improve the reliability of the identification. Recent research shows that person identification based on machine learning using keystroke dynamics data works surprisingly well. This is because the dynamics of typing is characteristic to users and a user is hardly able to mimic the dynamics of typing of another user. In this paper, we propose to use a projection-based classification technique for the task of person identification based on keystroke dynamics.

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Notes

  1. 1.

    See Sect. 2 for an overview of related works.

  2. 2.

    http://scikit-learn.org.

  3. 3.

    We note that the task of person identification is different from person authentication, in case of which the user claims an identify and the system has to decide whether the true identity matches the claimed identity. The task of person identification is inherently more challenging compared with the person authentication task, therefore, we decided to evaluate the proposed approach in context of person identification.

  4. 4.

    We note that they were calculated under the assumption that the observations in the classification experiment are representative to real-world application scenarios. This includes (but it is not limited to) the assumption of a naive attacker. That is, we did not assume an “intelligent” attacker who would try to record and/or imitate the dynamics of the legitimate user. Instead, a naive attacker was assumed who simply steals a bank card (or the information printed on the card) and tries to use it for internet-based transactions without paying attention to imitate the owner’s dynamics of typing.

  5. 5.

    We also note that there is a trade-off between the aforementioned two types of error and, if required, recognition systems may be tuned in order to decrease one of them, while the other type of error may increase. Though in principle, such tuning is possible in case of projection-based classification as well (for example, based on the continuous output of logistic regression), this is left for future work.

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Acknowledgment

D. Neubrandt was supported by the “Új Nemzeti Kiválóság Program” (ÚNKP-16-1-1) of the “Emberi Erőforrások Minisztériuma”.

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Authors and Affiliations

  1. Department of Computer Science and Information Theory, Budapest University of Technology and Economics, Budapest, Hungary

    Dora Neubrandt

  2. Knowledge Discovery and Machine Learning, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany

    Krisztian Buza

Authors
  1. Dora Neubrandt
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  2. Krisztian Buza
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Corresponding author

Correspondence to Dora Neubrandt .

Editor information

Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

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Neubrandt, D., Buza, K. (2018). Projection-Based Person Identification. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-59162-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

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