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A New Approach to the Dynamic Signature Verification Aimed at Minimizing the Number of Global Features

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9693))

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Abstract

Identity verification using the dynamic signature is an important biometric issue. Its big advantage is that it is commonly socially acceptable. Verification based on so-called global features is one of the most effective methods used for this purpose. In this paper we propose an approach which minimises a number of the features used during verification process due to check how the number of features affects the classification result. The paper contains the simulation results for the public MCYT-100 database of the dynamic signatures.

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References

  1. Akimoto, T., Ogata, T.: Experimental development of a focalization mechanism in an integrated narrative generation system. J. Artif. Intell. Soft Comput. Res. 5(3), 177–188 (2015)

    Article  Google Scholar 

  2. Bartczuk, Ł.: Gene expression programming in correction modelling of nonlinear dynamic objects. Adv. Intell. Syst. Comput. 429, 125–134 (2016)

    Article  Google Scholar 

  3. Bartczuk, Ł., Przybył, A., Koprinkova-Hristova, P.: New method for nonlinear fuzzy correction modelling of dynamic objects. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 169–180. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  4. Bartczuk, Ł., Rutkowska, D.: Type-2 fuzzy decision trees. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 197–206. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Bartczuk, Ł., Rutkowska, D.: Medical diagnosis with Type-2 fuzzy decision trees. In: Kącki, E., Rudnicki, M., Stempczyńska, J. (eds.) Computers in Medical Activity. AISC, vol. 65, pp. 11–21. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Bello, O., Holzmann, J., Yaqoob, T., Teodoriu, C.: Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J. Artif. Intell. Soft Comput. Res. 5(2), 121–139 (2015)

    Article  Google Scholar 

  7. Chu, J.L., Krzyżak, A.: The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks. J. Artif. Intell. Soft Comput. Res. 4(1), 5–19 (2014)

    Article  Google Scholar 

  8. Cierniak, R., Rutkowski, L.: On image compression by competitive neural networks and optimal linear predictors. Sig. Process.: Image Commun. 156, 559–565 (2000)

    Google Scholar 

  9. 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 

  10. Cpałka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Trans. Neural Netw. 20, 701–714 (2009)

    Article  Google Scholar 

  11. Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Anal. Ser. A: Theory Methods Appl. 71, 1659–1672 (2009)

    Article  Google Scholar 

  12. Cpałka, K., Łapa, K., Przybył, A.: A new approach to design of control systems using genetic programming. Inf. Technol. Control 44(4), 433–442 (2015)

    Google Scholar 

  13. Cpałka, K., Łapa, K., Przybył, A., Zalasiński, M.: A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. Neurocomputing 135, 203–217 (2014)

    Article  Google Scholar 

  14. Cpałka, K., Rebrova, O., Nowicki, R., Rutkowski, L.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42, 706–720 (2013)

    Article  MATH  Google Scholar 

  15. Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation. WSEAS Trans. Syst. 4(9), 1450–1458 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  17. 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 

  18. Cpałka, K., Zalasiński, M., Rutkowski, L.: A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Appl. Soft Comput. (2016, in press). http://dx.doi.org/10.1016/j.asoc.2016.02.017

  19. Doroz, R., Porwik, P., Orczyk, T.: Dynamic signature verification method based on association of features with similarity measures. Neurocomputing 171, 921–931 (2016)

    Article  Google Scholar 

  20. Duda, P., Hayashi, Y., Jaworski, M.: On the strong convergence of the orthogonal series-type kernel regression neural networks in a non-stationary environment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 47–54. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  21. Duda, P., Jaworski, M., Pietruczuk, L.: On pre-processing algorithms for data stream. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 56–63. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  22. Dziwiński, P., Avedyan, E.D.: A new approach to nonlinear modeling based on significant operating points detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 364–378. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  23. Dziwiński, P., Bartczuk, Ł., Przybył, A., Avedyan, E.D.: A new algorithm for identification of significant operating points using swarm intelligence. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 349–362. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  24. Er, M.J., Duda, P.: On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011, Part I. LNCS, vol. 7203, pp. 443–450. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Faundez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recogn. 40, 981–992 (2007)

    Article  MATH  Google Scholar 

  26. 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 

  27. Fiérrez-Aguilar, J., Nanni, L., Lopez-Peñalba, J., Ortega-Garcia, J., Maltoni, D.: An on-line signature verification system based on fusion of local and global information. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 523–532. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  28. Gabryel, M., Cpałka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: Proceedings of the I Workshop on Genetic Fuzzy Systems, Granada, pp. 119–123 (2005)

    Google Scholar 

  29. Gabryel, M., Korytkowski, M., Scherer, R., Rutkowski, L.: Object detection by simple fuzzy classifiers generated by boosting. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 540–547. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  30. Gacto, M.J., Alcala, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181, 4340–4360 (2011)

    Article  MATH  Google Scholar 

  31. Gałkowski, T., Rutkowski, L.: Nonparametric recovery of multivariate functions with applications to system identification. Proc. IEEE 73(5), 942–943 (1985)

    Article  Google Scholar 

  32. Gałkowski, T., Rutkowski, L.: Nonparametric fitting of multivariate functions. IEEE Trans. Autom. Control AC–31(8), 785–787 (1986)

    Article  MATH  Google Scholar 

  33. Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R., Voloshynovskiy, S.: From single image to list of objects based on edge and blob detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 605–615. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  34. Held, P., Dockhorn, A., Kruse, R.: On merging and dividing social graphs. J. Artif. Intell. Soft Comput. Res. 5(1), 23–49 (2015)

    Article  Google Scholar 

  35. Huang, K., Hong, Y.: Stability and style-variation modeling for on-line signature verification. Pattern Recogn. 36, 2253–2270 (2003)

    Article  MATH  Google Scholar 

  36. Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recogn. 44, 2231–2240 (2011)

    Article  Google Scholar 

  37. Jimenez, F., Kanoh, M., Yoshikawa, T., Furuhashi, T., Nakamura, T.: Effect of robot utterances using onomatopoeia on collaborative learning. J. Artif. Intell. Soft Comput. Res. 4(2), 125–131 (2014)

    Article  Google Scholar 

  38. Khan, M.A.U., Khan, M.K., Khan, M.A.: Velocity-image model for online signature verification. IEEE Trans. Image Process. 15, 3540–3549 (2006)

    Article  Google Scholar 

  39. Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recogn. Lett. 26, 2400–2408 (2005)

    Article  Google Scholar 

  40. Korytkowski, M., Nowicki, R., Scherer, R.: Neuro-fuzzy rough classifier ensemble. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part I. LNCS, vol. 5768, pp. 817–823. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  41. Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  42. Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)

    Article  MathSciNet  Google Scholar 

  43. Koshiyama, A.S., Vellasco, M.M., Tanscheit, R.: GPFIS-control: a genetic fuzzy system for control tasks. J. Artif. Intell. Soft Comput. Res. 4(3), 167–179 (2014)

    Article  Google Scholar 

  44. Laskowski, Ł., Laskowska, M.: Probing of synthesis route. J. Solid State Chem. 220, 221–226 (2014)

    Article  Google Scholar 

  45. Laskowski, Ł., Laskowska, M., Bałanda, M., Fitta, M., Kwiatkowska, J., Dziliński, K., Karczmarska, A.: Raman and magnetic analysis. Microporous Mesoporous Mater. 200, 253–259 (2014)

    Article  Google Scholar 

  46. Laskowska, M., Laskowski, Ł., Jelonkiewicz, J.: SBA-15 mesoporous silica activated by metal ions-verification of molecular structure on the basis of Raman spectroscopy supported by numerical simulations. J. Mol. Struct. 1100, 21–26 (2015)

    Article  Google Scholar 

  47. Laskowska, M., Laskowski, Ł., 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.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 72–78. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  48. Lee, P.M., Hsiao, T.C.: Applying LCS to affective image classification in spatial-frequency domain. J. Artif. Intell. Soft Comput. Res. 4(2), 99–123 (2014)

    Article  Google Scholar 

  49. Lumini, A., Nanni, L.: Ensemble of on-line signature matchers based on overcomplete feature generation. Expert Syst. Appl. 36, 5291–5296 (2009)

    Article  Google Scholar 

  50. Łapa, K., Cpałka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 217–232. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  51. Łapa, K., Zalasiński, M., Cpałka, K.: A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 329–344. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  52. Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 523–534. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  53. Moon, J.H., Lee, S.G., Cho, S.Y., Kim, Y.S.: A hybrid online signature verification system supporting multi-confidential levels defined by data mining techniques. Int. J. Intell. Syst. Technol. Appl. 9, 262–273 (2010)

    Google Scholar 

  54. Nanni, L.: An advanced multi-matcher method for on-line signature verification featuring global features and tokenised random numbers. Neurocomputing 69, 2402–2406 (2006)

    Article  Google Scholar 

  55. Nanni, L., Lumini, A.: Advanced methods for two-class problem formulation for on-line signature verification. Neurocomputing 69, 854–857 (2006)

    Article  Google Scholar 

  56. Nanni, L., Lumini, A.: Ensemble of Parzen window classifiers for on-line signature verification. Neurocomputing 68, 217–224 (2005)

    Article  Google Scholar 

  57. Nanni, L., Maiorana, E., Lumini, A., Campisi, P.: Combining local, regional and global matchers for a template protected on-line signature verification system. Expert Syst. Appl. 37, 3676–3684 (2010)

    Article  Google Scholar 

  58. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.-J., Vivaracho, C., Escudero, D., Moro, Q.-I.: MCYT baseline corpus: a bimodal biometric database. IEE Proc.-Vis. Image Sig. Process. 150, 395–401 (2003)

    Article  Google Scholar 

  59. Rutkowski, L.: Identification of MISO nonlinear regressions in the presence of a wide class of disturbances. IEEE Trans. Inf. Theory 37(1), 214–216 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  60. Rutkowski, L.: Sequential estimates of probability densities by orthogonal series and their application in pattern classification. IEEE Trans. Syst. Man Cybern. 10(12), 918–920 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  61. Rutkowski, L.: On-line identification of time-varying systems by nonparametric techniques. IEEE Trans. Autom. Control 27(1), 228–230 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  62. Rutkowski, L.: On nonparametric identification with prediction of time-varying systems. IEEE Trans. Autom. Control 29(1), 58–60 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  63. Rutkowski, L.: A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Trans. Circuits Syst. 33(8), 812–818 (1986)

    Article  MATH  Google Scholar 

  64. Rutkowski, L.: Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Trans. Neural Netw. 15(4), 811–827 (2004)

    Article  MathSciNet  Google Scholar 

  65. Rutkowski, L.: Computational Intelligence. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  66. Rutkowski, L., Cpałka, K.: Flexible Structures of Neuro-Fuzzy Systems. Quo Vadis Computational Intelligence. Studies in Fuzziness and Soft Computing, vol. 54. Springer, Heidelberg (2000)

    Google Scholar 

  67. Rutkowski, L., Cpałka, K.: Compromise approach to neuro-fuzzy systems. In: Sincak, P., Vascak, J., Kvasnicka, V., Pospichal, J. (eds.) Intelligent Technologies - Theory and Applications, vol. 76, pp. 85–90. IOS Press (2002)

    Google Scholar 

  68. Rutkowski L., Cpałka K.: Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP 2002), Orchid Country Club, Singapore, 18–22 November 2002

    Google Scholar 

  69. Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Trans. Neural Netw. 14, 554–574 (2003)

    Article  Google Scholar 

  70. Rutkowski, L., Cpałka, K.: Neuro-fuzzy systems derived from quasi-triangular norms. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Budapest, 26–29 July 2004, vol. 2, pp. 1031–1036 (2004)

    Google Scholar 

  71. Rutkowski, L., Cpałka, K.: Designing and learning of adjustable quasi triangular norms with applications to neuro-fuzzy systems. IEEE Trans. Fuzzy Syst. 13, 140–151 (2005)

    Article  Google Scholar 

  72. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)

    Article  Google Scholar 

  73. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014)

    Article  Google Scholar 

  74. Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)

    Article  Google Scholar 

  75. Rutkowski, L., Przybył, A., Cpałka, K.: Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. IEEE Trans. Ind. Electron. 59, 1238–1247 (2012)

    Article  Google Scholar 

  76. Rutkowski, L., Rafajłowicz, E.: On optimal global rate of convergence of some nonparametric identification procedures. IEEE Trans. Autom. Control 34(10), 1089–1091 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  77. Scherer, R.: Neuro-fuzzy systems with relation matrix. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS, vol. 6113, pp. 210–215. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  78. Scherer, R., Rutkowski, L.: Relational equations initializing neuro-fuzzy system. In: Proceedings of the 10th Zittau Fuzzy Colloquium, Zittau, Germany, pp. 18–22 (2002)

    Google Scholar 

  79. Starczewski, J.T., Bartczuk, Ł., Dziwiński, P., Marvuglia, A.: Learning methods for Type-2 FLS based on FCM. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS, vol. 6113, pp. 224–231. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  80. Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, R., Szyprowski, J.: Application of neural networks in assessing changes around implant after total hip arthroplasty. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 335–340. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  81. Szarek, A., Korytkowski, M., Rutkowski, L., Scherer, R., Szyprowski, J.: Forecasting wear of head and acetabulum in hip joint implant. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 341–346. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  82. Szczypta, J., Przybył, A., Wang, L.: Evolutionary approach with multiple quality criteria for controller design. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 455–467. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  83. Smyczyńska, J., Hilczer, M., Smyczyńska, U., Stawerska, R., Tadeusiewic, R., Lewiński, A.: Artificial neural models - a novel tool for predictying the efficacy of growth hormone (GH) therapy in children with short stature. Neuroendocrinol. Lett. (ISSN: 0172-780X, ISSN-L: 0172-780X) 36(4), 348–353 (2015)

    Google Scholar 

  84. Smyczyńska, U., Smyczyńska, J., Hilczer, M., Stawerska, R., Lewiński, A., Tadeusiewicz, R.: Artificial neural networks - a novel tool in modelling the effectiveness of growth hormone (GH) therapy in children with GH deficiency. Pediatr. Endocrinol. 14(2), 9–18 (2015)

    Article  Google Scholar 

  85. Starczewski, J.T., Rutkowski, L.: Connectionist structures of Type 2 fuzzy inference systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, p. 634. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  86. Tadeusiewicz, R.: Neural networks as a tool for modeling of biological systems. Bio-Algorithms Med-Syst. 11(3), 135–144 (2015)

    Google Scholar 

  87. Tadeusiewicz, R.: Neural networks in mining sciences - general overview and some representative examples. Arch. Min. Sci. 60(4), 971–984 (2015)

    Google Scholar 

  88. Wang, X., Liu, X., Japkowicz, N., Matwin, S.: Automated approach to classification of mine-like objects using multiple-aspect sonar images. J. Artif. Intell. Soft Comput. Res. 4(2), 133–148 (2014)

    Article  Google Scholar 

  89. 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 

  90. Zalasiński, M., Łapa, K., Cpałka, K.: New algorithm for evolutionary selection of the dynamic signature global features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 113–121. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  91. Zalasiński, M., Cpałka, K.: A New Method of On-line Signature Verification Using a Flexible Fuzzy One-Class Classifier, pp. 38–53. Academic Publishing House EXIT (2011)

    Google Scholar 

  92. 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, vol. 7268, pp. 362–367. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  93. Zalasiński, M., Cpałka, K.: New approach for the on-line signature verification based on method of horizontal partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 342–350. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  94. Zalasiński, M., Cpałka, K., Er, M.J.: New method for dynamic signature verification using hybrid partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 216–230. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  95. Zalasiński, M., Cpałka, K., Er, M.J.: A new method for the dynamic signature verification based on the stable partitions of the signature. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 161–174. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  96. Zalasiński, M., Cpałka, K., Hayashi, Y.: New method for dynamic signature verification based on global features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 231–245. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  97. 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.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 175–188. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  98. Zhao, W., Lun, R., Espy, D.D., Ann, R.M.: Realtime motion assessment for rehabilitation exercises: integration of kinematic modeling with fuzzy inference. J. Artif. Intell. Soft Comput. Res. 4(4), 267–285 (2014)

    Article  Google Scholar 

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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.

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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 .

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Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Krakow, 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. (2016). A New Approach to the Dynamic Signature Verification Aimed at Minimizing the Number of Global Features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_20

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  • DOI: https://doi.org/10.1007/978-3-319-39384-1_20

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