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A Method for Changes Prediction of the Dynamic Signature Global Features over Time

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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 can be represented by a set of global features. These features are interpreted as e.g. number of pen ups, time of signing process, etc. Values of global features can be determined on the basis of non-linear waveforms defining dynamics of the signature. They are acquired using graphic tablet or a device with a touch screen. In this paper we present a method for prediction values of the dynamic signature global features changing over time. The purpose of the prediction is, among others, improving the efficiency of the dynamic signature verification process when the interval between acquisition sessions is large. This interval causes a slight change in the way of signing, which can affect change in the value of global features. In this case the effectiveness of the signature verification also changes (decreases). The possibility of predicting the values of global features can result in a partial elimination of the described problem. Tests of the proposed method were performed using ATVS-SLT DB database of the dynamic signatures.

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Acknowledgments

The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.

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Authors and Affiliations

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

    Marcin Zalasiński, Krystian Łapa & Krzysztof Cpałka

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

    Takamichi Saito

Authors
  1. Marcin Zalasiński
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  2. Krystian Łapa
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  3. Krzysztof Cpałka
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  4. Takamichi Saito
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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., Łapa, K., Cpałka, K., Saito, T. (2017). A Method for Changes Prediction of the Dynamic Signature Global Features over Time. 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_68

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  • DOI: https://doi.org/10.1007/978-3-319-59063-9_68

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