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Signature Partitioning Using Selected Population-Based Algorithms

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

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Abstract

Dynamic signature is a biometric attribute which is commonly used for identity verification. Artificial intelligence methods, especially population-based algorithms (PBAs), can be very useful in the dynamic signature verification process. They are able to, among others, support selection of the most characteristic descriptors of the signature or perform signature partitioning. In this paper, we focus on creating the most characteristic signature partitions using different PBAs and comparing their effectiveness. The simulations whose results are presented in this paper were performed using the BioSecure DS2 database distributed by the BioSecure Association.

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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.

Author information

Authors and Affiliations

  1. Department of Computational Intelligence, Czestochowa University of Technology, Częstochowa, Poland

    Marcin Zalasiński & Krzysztof Cpałka

  2. Department of Marine Mechatronics, Gdask University of Technology, Gdańsk, Poland

    Tacjana Niksa-Rynkiewicz

  3. Department of Computer Science, Meiji University, Tokyo, Japan

    Yoichi Hayashi

Authors
  1. Marcin Zalasiński
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  2. Krzysztof Cpałka
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  3. Tacjana Niksa-Rynkiewicz
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  4. Yoichi Hayashi
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Corresponding author

Correspondence to Marcin Zalasiński .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

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

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Zalasiński, M., Cpałka, K., Niksa-Rynkiewicz, T., Hayashi, Y. (2020). Signature Partitioning Using Selected Population-Based Algorithms. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_44

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_44

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