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A Method for Genetic Selection of the Most Characteristic Descriptors of the Dynamic Signature

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

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Abstract

Dynamic signature verification is an important area of biometrics. In this area methods from the field of computational intelligence can be used. In this paper we propose a new method for genetic selection of the most characteristic descriptors of the dynamic signature. The descriptors are global features of the signature and components created within its partitions. Selection of the descriptors is realized individually for each user of the biometric system. Its purpose is to increase the precision of the biometric system by eliminating the descriptors which do not increase efficiency of verification procedure. Number of descriptors (their combination) can be high, so the use of genetic algorithm to reduce their number seems to be justified. Moreover, reduction of descriptors increases interpretability of fuzzy mechanism for evaluation of signatures’ similarity. Proposed method was tested using known dynamic signatures database-MCYT-100.

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Acknowledgment

The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138. The work presented in this paper was also supported by the grant number BS/MN 1-109-301/16/P.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Marcin Zalasiński & Krzysztof Cpałka

  2. 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. 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

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

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

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Zalasiński, M., Cpałka, K., Hayashi, Y. (2017). A Method for Genetic Selection of the Most Characteristic Descriptors of the Dynamic Signature. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_67

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  • DOI: https://doi.org/10.1007/978-3-319-59063-9_67

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