Skip to main content
SpringerLink
Log in

Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12855))

Included in the following conference series:

  • 759 Accesses

Abstract

The dynamic signature is a biometric attribute used for identity verification. It contains information on dynamics of the signing process. There are many approaches to the dynamic signature verification, including the one based on signature partitioning. Partitions are the regions created on the basis of signals describing the dynamics of the signature. They contain information on the shape of the signature characteristic of a given individual. In this paper, we focus on so-called vertical partitioning and different population-based algorithms which are used to determine partition division points. In the verification process we use an authorial one-class classifier.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 7, pp. 4661–4666 (2007)

    Google Scholar 

  2. Bartczuk, Ł., Dziwiński, P., Goetzen, P.: Nonlinear fuzzy modelling of dynamic objects with fuzzy hybrid particle swarm optimization and genetic algorithm. In: International Conference on Artificial Intelligence and Soft Computing, pp. 315–325. Springer, Cham (2020)

    Google Scholar 

  3. Bartczuk, Ł, Przybył, A., Cpałka, K.: A new approach to nonlinear modelling of dynamic systems based on fuzzy rules. Int. J. Appl. Math. Comput. Sci. (AMCS) 26(3), 603–621 (2016)

    Article  MathSciNet  Google Scholar 

  4. Bilski, J., Kowalczyk, B., Żurada, J.M.: Application of the givens rotations in the neural network learning algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9692, pp. 46–56. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39378-0_5

    Chapter  Google Scholar 

  5. Bilski, J., Kowalczyk, B., Żurada, J.M.: Parallel implementation of the givens rotations in the neural network learning algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 14–24. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_2

    Chapter  Google Scholar 

  6. Bilski, J., Rutkowski, L., Smoląg, J., Tao, D.: A novel method for speed training acceleration of recurrent neural networks. Inf. Sci. 553, 266–279 (2021). https://doi.org/10.1016/j.ins.2020.10.025

  7. Cpałka, K.: Design of Interpretable Fuzzy Systems. Springer, Cham (2017)

    Google Scholar 

  8. Cpalka, K., Łapa, K., Przybył, A.: Genetic programming algorithm for designing of control systems. Inf. Technol. Control 47(4), 668–683 (2018)

    Google Scholar 

  9. Cpałka, K., Zalasiński, M.: On-line signature verification using vertical signature partitioning. Expert Syst. Appl. 41, 4170–4180 (2014)

    Article  Google Scholar 

  10. Cpałka, K., Zalasiński, M., Rutkowski, L.: New method for the on-line signature verification based on horizontal partitioning. Pattern Recogn. 47, 2652–2661 (2014)

    Article  Google Scholar 

  11. Dziwiński, P., Bartczuk, Ł, Paszkowski, J.: A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm. J. Artif. Intell. Soft Comput. Res. 10, 95–111 (2020)

    Article  Google Scholar 

  12. Dziwiński, P., Bartczuk, Ł, Przybyszewski, K.: A population based algorithm and fuzzy decision trees for nonlinear modeling. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS (LNAI), vol. 10842, pp. 516–531. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91262-2_46

    Chapter  Google Scholar 

  13. Dziwiński, P., Bartczuk, Ł: A new hybrid particle swarm optimization and genetic algorithm method controlled by fuzzy logic. IEEE Trans. Fuzzy Syst. 28(6), 1140–1154 (2019)

    Article  Google Scholar 

  14. Ferdaus, M.M., Anavatti, S.G., Garratt, M.A., Pratamam, M.: Development of C-Means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. J. Artif. Intell. Soft Comput. Res. 9, 99–109 (2020)

    Article  Google Scholar 

  15. Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzalez-Rodriguez, J.: HMM-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn. Lett. 28, 2325–2334 (2007)

    Article  Google Scholar 

  16. Homepage of Association BioSecure. http://biosecure.it-sudparis.eu. Accessed 13 Nov 2020

  17. Hou, Y., Holder, L.B.: On graph mining with deep learning: introducing model R for link weight prediction. J. Artif. Intell. Soft Comput. Res. 9, 21–40 (2019)

    Article  Google Scholar 

  18. Houmani, N., et al.: BioSecure signature evaluation campaign (BSEC 2009): evaluating online signature algorithms depending on the quality of signatures. Pattern Recogn. 45, 993–1003 (2012)

    Article  Google Scholar 

  19. Jain, A.K., Ross, A.: Introduction to biometrics. In: Jain, A.K., Flynn, P., Ross, A.A. (eds.) Handbook of Biometrics. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  20. Korytkowski, M., Senkerik, R., Scherer, M.M., Angryk, K.M., Siwocha, A.: Efficient image retrieval by fuzzy rules from boosting and metaheuristic. J. Artif. Intell. Soft Comput. Res. 10, 57–69 (2020)

    Article  Google Scholar 

  21. Krell, E., Sheta, A., Balasubramanian, A.P.R., King, S.A.: Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning. J. Artif. Intell. Soft Comput. Res. 9, 267–282 (2019)

    Article  Google Scholar 

  22. Laskowski, Ł, Laskowska, M., Jelonkiewicz, J., Boullanger, A.: Molecular approach to Hopfield neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9119, pp. 72–78. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19324-3_7

    Chapter  Google Scholar 

  23. Laskowski, Ł.: Objects auto-selection from stereo-images realised by self-correcting neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS (LNAI), vol. 7267, pp. 119–125. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29347-4_14

  24. Laskowski, Ł.: Hybrid-maximum neural network for depth analysis from stereo-image. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 47–55. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13232-2_7

  25. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  26. Nasim, A., Burattini, L., Fateh, M.F., Zameer, A.: Solution of linear and non-linear boundary value problems using population-distributed parallel differential evolution. J. Artif. Intell. Soft Comput. Res. 9, 205–218 (2019)

    Article  Google Scholar 

  27. Osaba, E., Diaz, F., Onieva, E.: Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl. Intell. 41, 145–166 (2014)

    Article  Google Scholar 

  28. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)

    Google Scholar 

  29. Rutkowski, L.: On Bayes risk consistent pattern recognition procedures in a quasi-stationary environment. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-4(1) 84–87 (1982)

    Google Scholar 

  30. Rutkowski, L.: Sequential pattern recognition procedures derived from multiple Fourier series. Pattern Recogn. Lett. 8(4), 213–216 (1988)

    Article  Google Scholar 

  31. Shewalkar, A., Nyavanandi, D., Ludwig, S.A.: Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J. Artif. Intell. Soft Comput. Res. 9, 235–245 (2019)

    Article  Google Scholar 

  32. Souza, G.B., Silva Santos, D.F., Pires, R.G., Marananil, A.N., Papa, J.P.: Deep features extraction for robust fingerprint spoofing attack detection. J. Artif. Intell. Soft Comput. Res. 9, 41–49 (2019)

    Article  Google Scholar 

  33. Starczewski, J.T., Goetzen, P., Napoli, C.: Triangular fuzzy-rough set based fuzzification of fuzzy rule-based systems. J. Artif. Intell. Soft Comput. Res. 10, 271–285 (2020)

    Article  Google Scholar 

  34. Szczypta, J., Przybył, A., Cpałka, K.: Some aspects of evolutionary designing optimal controllers. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7895, pp. 91–100. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38610-7_9

    Chapter  Google Scholar 

  35. Tambouratzis, G., Vassiliou, M.: Swarm algorithms for NLP-the case of limited training data. J. Artif. Intell. Soft Comput. Res. 9, 219–234 (2019)

    Article  Google Scholar 

  36. Zalasiński, M., Cpałka, K., Hayashi, Y.: New fast algorithm for the dynamic signature verification using global features values. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9120, pp. 175–188. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19369-4_17

    Chapter  Google Scholar 

Download references

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, Gdańsk University of Technology, Gdańsk, Poland

    Tacjana Niksa-Rynkiewicz

Authors
  1. Marcin Zalasiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tacjana Niksa-Rynkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krzysztof Cpałka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marcin Zalasiński .

Editor information

Editors and Affiliations

  1. Czestochowa 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. Edmonton, AB, Canada

    Witold Pedrycz

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

    Ryszard Tadeusiewicz

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

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zalasiński, M., Niksa-Rynkiewicz, T., Cpałka, K. (2021). Dynamic Signature Vertical 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 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87897-9_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87896-2

  • Online ISBN: 978-3-030-87897-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation