Skip to main content

Novel Algorithm for the On-Line Signature Verification Using Selected Discretization Points Groups

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

Abstract

Identity verification based on on-line signature is a commonly known biometric task. Some methods based on the on-line signature biometric attribute used for identity verification use information from partitions of the signature. Efficiency of these methods is relatively high. In this paper we would like to present a new approach to signature trajectories partitioning, based on selection of the discretization points groups. The new method was compared to other methods, with use of the SVC2004 public on-line signature database.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cpałka, K., Rutkowski, L.: A new method for designing and reduction of neuro-fuzzy systems. In: IEEE Int. Conference on Fuzzy Systems, pp. 1851–1857 (2006)

    Google Scholar 

  2. Cpalka, K.: A method for designing flexible neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 212–219. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Cpałka, K., Rutkowski, L.: Neuro-fuzzy structures for pattern classification. WSEAS Trans. on Computers, 697–688 (2005)

    Google Scholar 

  4. Cpałka, K., Rutkowski, L.: Neuro-fuzzy systems derived from quasi-triangular norms. In: IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1031–1036 (2004)

    Google Scholar 

  5. Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Analysis series A: Theory, Methods & Applications, vol. 71. Elsevier (2009)

    Google Scholar 

  6. Faundez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40 (2007)

    Google Scholar 

  7. Gabryel, M., Rutkowski, L.: Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 398–404. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Greblicki, W., Rutkowski, L.: Density-free Bayes risk consistency of nonparametric pattern recognition procedures. Proceedings of the IEEE 69(4), 482–483 (1981)

    Article  Google Scholar 

  9. Ibrahim, M.T., Khan, M.A., Alimgeer, K.S., Khan, M.K., Taj, I.A., Guan, L.: Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification. Pattern Recognition 43 (2010)

    Google Scholar 

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

    Google Scholar 

  11. Khan, M.A.U., Khan, M.K., Khan, M.A.: Velocity-image model for online signature verification. IEEE Trans. Image Process 15 (2006)

    Google Scholar 

  12. Korytkowski, M., Gabryel, M., Rutkowski, L., Drozda, S.: Evolutionary methods to create interpretable modular system. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 405–413. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Korytkowski, M., Rutkowski, L., Scherer, R.: On combining backpropagation with boosting. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vancouver, pp. 1274–1277 (2005)

    Google Scholar 

  14. Li, X., Er, M.J., Lim, B.S., et al.: Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection. International Journal of Neural Systems 20(5), 405–419 (2010)

    Article  Google Scholar 

  15. Nowicki, R., Pokropińska, A.: Information criterions applied to neuro-fuzzy architectures design. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 332–337. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  16. Pravdova, V., Walczak, B., Massart, D.L.: A comparison of two algorithms for warping of analytical signals. Analytica Chimica Acta 456, 77–92 (2002)

    Article  Google Scholar 

  17. Przybył, A., Cpałka, K.: A new method to construct of interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 697–705. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  18. Rutkowska, D., Nowicki, R., Rutkowski, L.: Neuro-fuzzy architectures with various implication operators. In: Sincak, P., et al. (eds.) The State of the Art in Computational Intelligence, pp. 214–219 (2000)

    Google Scholar 

  19. Rutkowski, L.: Sequential estimates of probability densities by orthogonal series and their application in pattern classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-10(12), 918–920 (1980)

    MathSciNet  Google Scholar 

  20. Rutkowski, L.: On Bayes risk consistent pattern recognition procedures in a quasi-stationary environment. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-4(1), 84–87 (1982)

    Article  MathSciNet  Google Scholar 

  21. Rutkowski, L.: Adaptive probabilistic neural-networks for pattern classification in time-varying environment. IEEE Transactions on Neural Networks 15, 811–827 (2004)

    Article  Google Scholar 

  22. Rutkowski, L.: Computational intelligence. Springer (2007)

    Google Scholar 

  23. Rutkowski, L., Cpałka, K.: Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002, pp. 1857–1861 (2002)

    Google Scholar 

  24. Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online Speed Profile Generation for Industrial Machine Tool Based on Neuro Fuzzy Approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  25. Rutkowski, L., Przybył, A., Cpałka, K.: Novel Online Speed Profile Generation for Industrial Machine Tool Based on Flexible Neuro-Fuzzy Approximation. IEEE Transactions on Industrial Electronics 59(2), 1238–1247 (2012)

    Article  Google Scholar 

  26. Scherer, R., Rutkowski, L.: A fuzzy relational system with linguistic antecedent certainty factors. In: 6th International Conference on Neural Networks and Soft Computing, Zakopane, Poland. Advances In Soft Computing, pp. 563–569 (2003)

    Google Scholar 

  27. Scherer, R., Rutkowski, L.: Connectionist fuzzy relational systems. In: Halgamuge, S.K., Wang, L. (eds.) Computational Intelligence for Modelling and Prediction. SCI, vol. 2, pp. 35–47. Springer, Heidelberg (2005)

    Google Scholar 

  28. Scherer, R., Rutkowski, L.: Neuro-fuzzy relational classifiers. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 376–380. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  29. Yeung, D.-Y., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T., Rigoll, G.: SVC2004: First International Signature Verification Competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 16–22. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  30. Zalasiński, M., Cpałka, K.: A new method of on-line signature verification using a flexible fuzzy one-class classifier. Selected Topics in Computer Science Applications, pp. 38–53. EXIT (2011)

    Google Scholar 

  31. Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS (LNAI), vol. 7268, pp. 362–367. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zalasiński, M., Cpałka, K. (2013). Novel Algorithm for the On-Line Signature Verification Using Selected Discretization Points Groups. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38658-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics