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Dynamic Signature Verification Using Selected Regions

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13589))

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Abstract

Identity verification takes into account biometric attributes. Behavioral ones are particularly important. One of them is a dynamic signature. An analysis of such type of a signature uses signals describing the signing process. In this paper, we consider the velocity signal and propose a new method for dividing the dynamic signature into groups. In this case, a group is a subset of consecutive discretization points corresponding to similar velocity values. We have also assumed here that the signature fragments characterized by the highest pen velocity are the most characteristic of each user, therefore we reject partitions related to medium and low velocity values. As a result, we individually create a unique set of partitions of different sizes for each user. We do not use skilled forgeries, which is an additional advantage of our approach. The proposed method has been tested using the BioSecure dynamic signature database. The obtained results have confirmed the effectiveness of the proposed approach.

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Acknowledgment

This paper was financed under the program of the Minister of Science and Higher Education under the name ’Regional Initiative of Excellence’ in the years 2019–2022, project number 020/RID/2018/19 with the amount of financing PLN 12 000 000.

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Authors and Affiliations

  1. University of Lower Silesia, Wrocław, Poland

    Stanisław Lota

  2. Department of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Marcin Zalasiński, Piotr Duda & Krzysztof Cpałka

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  1. Marcin Zalasiński
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  2. Piotr Duda
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  3. Stanisław Lota
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  4. Krzysztof Cpałka
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Correspondence to Marcin Zalasiński .

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Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Zalasiński, M., Duda, P., Lota, S., Cpałka, K. (2023). Dynamic Signature Verification Using Selected Regions. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_33

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