Abstract
Identity verification based on the dynamic signature is an important issue of biometrics. There are many effective methods to the signature verification which take into account the dynamic characteristics of the signature (e.g. velocity of the pen, the pen’s pressure on the surface of the graphic tablet, etc.). Among these methods, the ones based on the so-called global features are very important. In our previous paper we have proposed new algorithm for evolutionary selection of the dynamic signature global features, which selects a subset of features individually for each user. Algorithm proposed in this paper is a faster version of the method proposed earlier. During development of the algorithm we resigned from using evolutionary selection of global features and standardized working of the classifier in the context of all users. The paper contains the simulation results for the BioSecure database of the dynamic signatures.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bartczuk, Ł., Dziwiński, P., Starczewski, J.T.: New method for generationtype-2 fuzzy partition for FDT. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS (LNAI), vol. 6113, pp. 275–280. Springer, Heidelberg (2010)
Bartczuk, Ł., Dziwiński, P., Starczewski, J.T.: A new method for dealing with unbalanced linguistic term set. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 207–212. Springer, Heidelberg (2012)
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 (LNAI), vol. 8467, pp. 169–180. Springer, Heidelberg (2014)
Bhattacharya, I., Ghosh, P., Biswas, S.: Offline Signature Verification Using Pixel Matching Technique. Procedia Technology 10, 970–977 (2013)
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)
Cpałka, K.: A New Method for Design and Reduction of Neuro-Fuzzy Classification Systems. IEEE Trans. on Neural Networks 20, 701–714 (2009)
Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Analysis Series A: Theory, Methods and Applications 71, 1659–1672 (2009)
Cpałka, K., Rebrova, O., Nowicki, R., Rutkowski, L.: On Design of Flexible Neuro-Fuzzy Systems for Nonlinear Modelling. International Journal of General Systems 42, 706–720 (2013)
Cpałka, K., Zalasiński, M.: On-line signature verification using vertical signature partitioning. Expert Systems with Applications 41, 4170–4180 (2014)
Cpałka, K., Zalasiński, M., Rutkowski, L.: New method for the on-line signature verification based on horizontal partitioning. Pattern Recognition 47, 2652–2661 (2014)
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 (LNAI), vol. 8468, pp. 349–362. Springer, Heidelberg (2014)
Faundez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40, 981–992 (2007)
Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzalez-Rodriguez, J.: HMM-based on-line signature verification: Feature extraction and signature modeling. Pattern Recognition Letters 28, 2325–2334 (2007)
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)
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 (LNAI), vol. 7894, pp. 540–547. Springer, Heidelberg (2013)
Gacto, M.J., Alcala, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences 181, 4340–4360 (2011)
Gałkowski, T.: Kernel Estimation of Regression Functions in the Boundary Regions. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS (LNAI), vol. 7895, pp. 158–166. Springer, Heidelberg (2013)
Galkowski, T., Pawlak, M.: Nonparametric function fitting in the presence of nonstationary noise. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS (LNAI), vol. 8467, pp. 531–538. Springer, Heidelberg (2014)
Galkowski, T., Rutkowski, L.: Nonparametric fitting of multivariate functions. IEEE Trans. Automatic Control AC-31(8), 785–787 (1986)
Greblicki, W., Rutkowski, L.: Density-free Bayes risk consistency of nonparametric pattern recognition procedures. Proc. of the IEEE 69(4), 482–483 (1981)
Greenfield, S., Chiclana, F.: Type-reduction of the discretized interval type-2 fuzzy set: approaching the continuous case through progressively finer discretization. Journal of Artificial Intelligence and Soft Computing Research 1(3), 183–193 (2011)
Grycuk, R., Gabryel, M., Korytkowski, M., Scherer, R.: Content-Based Image Indexing by Data Clustering and Inverse Document Frequency. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B. z. (eds.) BDAS 2014. CCIS, vol. 424, pp. 374–383. Springer, Heidelberg (2014)
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 (LNAI), vol. 8468, pp. 605–615. Springer, Heidelberg (2014)
Homepage of Association BioSecure, http://biosecure.it-sudparis.eu (accessed: December 20, 2014)
Houmani, N., Garcia-Salicetti, S., Mayoue, A., Dorizzi, B.: BioSecure Signature Evaluation Campaign 2009 (BSEC 2009): Results (2009), http://biometrics.it-sudparis.eu/BSEC2009/downloads/BSEC2009_results.pdf (accessed: December 20, 2014)
Huang, K., Hong, Y.: Stability and style-variation modeling for on-line signature verification. Pattern Recognition 36, 2253–2270 (2003)
Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognition 44, 2231–2240 (2011)
Khan, M.A.U., Khan, M.K., Khan, M.A.: Velocity-image model for online signature verification. IEEE Trans. on Image Process. 15, 3540–3549 (2006)
Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognition Letters 26, 2400–2408 (2005)
Korytkowski, M., Nowicki, R., Rutkowski, L., Scherer, R.: AdaBoost Ensemble of DCOG Rough–Neuro–Fuzzy Systems. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 62–71. Springer, Heidelberg (2011)
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)
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)
Kumar, R., Sharma, J.D., Chanda, B.: Writer-independent off-line signature verification using surroundedness feature. Pattern Recognition Letters 33, 301–308 (2012)
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, Part II. LNCS (LNAI), vol. 6114, pp. 47–55. Springer, Heidelberg (2010)
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, Part I. LNCS, vol. 7267, pp. 119–125. Springer, Heidelberg (2012)
Lee, L.L., Berger, T., Aviczer, E.: Reliable on-line human signature verification systems. IEEE Trans. on Pattern Anal. and Machine Intell. 18, 643–647 (1996)
Lumini, A., Nanni, L.: Ensemble of on-line signature matchers based on overcomplete feature generation. Expert Systems with Applications 36, 5291–5296 (2009)
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. International Journal of Intelligent Systems Technologies and Applications 9, 262–273 (2010)
Nanni, L., Lumini, A.: Ensemble of Parzen window classifiers for on-line signature verification. Neurocomputing 68, 217–224 (2005)
Nanni, L., Maiorana, E., Lumini, A., Campisi, P.: Combining local, regional and global matchers for a template protected on-line signature verification system. Expert Systems with Applications 37, 3676–3684 (2010)
Nelson, W., Kishon, E.: Use of dynamic features for signature verification. In: Proc. of the IEEE Intl. Conf. on Systems, Man, and Cyber., vol. 1, pp. 201–205 (1991)
Nelson, W., Turin, W., Hastie, T.: Statistical methods for on-line signature verification. Intl. Journal of Pattern Recognition and Artificial Intell. 8, 749–770 (1994)
Nowicki, R.: Rough-Neuro-Fuzzy System with MICOG Defuzzification. In: IEEE International Conference on Fuzzy Systems, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, July 16-21, pp. 1958–1965 (2006)
Nowicki, R.: Rough-neuro-fuzzy structures for classification with missing data. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 39(6), 1334–1347 (2009)
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)
Nowicki, R., Scherer, R., Rutkowski, L.: A Method For Learning Of Hierarchical Fuzzy Systems. In: Sincak, P., et al. (eds.) Intelligent Technologies - Theory and Applications, pp. 124–129. IOS Press, Amsterdam (2002)
Pabiasz, S., Starczewski, J.: Face reconstruction for 3d systems. In: Rutkowska, D., Cader, A., Przybyszewski, K. (eds.) Selected Topics in Computer Science Applications,, pp. 54–63. Academic Publishing House EXIT (2011)
Pabiasz, S., Starczewski, J.T.: Meshes vs. Depth maps in face recognition systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 567–573. Springer, Heidelberg (2012)
Pławiak, P., Tadeusiewicz, R.: Approximation of phenol concentration using novel hybrid computational intelligence methods. Applied Mathematics and Computer Science 24(1) (2014)
Patan, K., Patan, M.: Optimal Training strategies for locally recurrent neural networks. Journal of Artificial Intelligence and Soft Computing Research 1(2), 103–114 (2011)
Peteiro-Barral, D., Bardinas, B.G., Perez-Sanchez, B.: Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms. Journal of Artificial Intelligence and Soft Computing Research 2(1), 5–20 (2012)
Przybył, A., Er, M.J.: The idea for the integration of neuro-fuzzy hardware emulators with real-time network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS (LNAI), vol. 8467, pp. 279–294. Springer, Heidelberg (2014)
Przybył, A., Jelonkiewicz, J.: Genetic algorithm for observer parameters tuning in sensorless induction motor drive. In: Neural Networks and Soft Computing, pp. 376–381 (2003)
Przybył, A., Smoląg, J., Kimla, P.: Distributed control system based on real time ethernet for computer numerical controlled machine tool (in Polish). Przegląd Elektrotechniczny 86(2), 342–346 (2010)
Rutkowski, L.: Sequential estimates of probability densities by orthogonal series and their application in pattern-classification. IEEE Trans. Systems Man and Cybernetics 10(12), 918–920 (1980)
Rutkowski, L.: Sequential pattern-recognition procedures derived from multiple Fourier-series. Pattern Recognition Letters 8(4), 213–216 (1988)
Rutkowski, L.: Application of multiple Fourier-series to identification of multivariable non-stationary systems. Int. Journal of Systems Science 20(10), 1993–2002 (1989)
Rutkowski, L.: Identification of miso nonlinear regressions in the presence of a wide class of disturbances. IEEE Trans. Information Theory 37(1), 214–216 (1991)
Rutkowski, L.: Computational intelligence. Springer (2008)
Rutkowski, L., Cpałka, K.: Flexible structures of neuro-fuzzy systems. In: Sincak, P., Vascak, J. (eds.) Quo Vadis Computational Intelligence. STUDFUZZ, vol. 54, pp. 479–484. Springer, Heidelberg (2000)
Rutkowski, L., Cpałka, K.: Compromise approach to neuro-fuzzy systems. In: Sincak, J., Vascak, V., Kvasnicka, J. (eds.) Intelligent Technologies - Theory and Applications, vol. 76, pp. 85–90. IOS Press (2002)
Rutkowski, L., Cpałka, K.: Flexible neuro-fuzzy systems. IEEE Trans. on Neural Networks 14, 554–574 (2003)
Rutkowski, L., Cpałka, K.: Designing and learning of adjustable quasi triangular norms with applications to neuro-fuzzy systems. IEEE Trans. on Fuzzy Systems 13, 140–151 (2005)
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision Trees for Mining Data Streams Based on the Gaussian Approximation. IEEE Transactions on Knowledge and Data Engineering 26, 108–119 (2014)
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Information Sciences 266, 1–15 (2014)
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 (LNAI), vol. 6114, pp. 645–650. Springer, Heidelberg (2010)
Rutkowski, L., Przybył, A., Cpałka, K.: Novel Online Speed Profile Generation for Industrial Machine Tool Based on Flexible Neuro-Fuzzy Approximation. IEEE Trans. on Industrial Electronics 59, 1238–1247 (2012)
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 (LNAI), vol. 6113, pp. 224–231. Springer, Heidelberg (2010)
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)
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)
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, Part II. LNCS, vol. 7895, pp. 91–100. Springer, Heidelberg (2013)
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)
Tadeusiewicz, R., Chaki, R., Chaki, N.: Exploring Neural Networks with C#. CRC Press, Taylor & Francis Group, Boca Raton (2014)
Yeung, D.-Y., Chang, H., Xiong, Y., George, S.E., Kashi, R.S., 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)
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 (LNAI), vol. 7895, pp. 113–121. Springer, Heidelberg (2013)
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)
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)
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 (LNAI), vol. 7895, pp. 342–350. Springer, Heidelberg (2013)
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 (LNAI), vol. 8468, pp. 231–245. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zalasiński, M., Cpałka, K., Hayashi, Y. (2015). New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-19369-4_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19368-7
Online ISBN: 978-3-319-19369-4
eBook Packages: Computer ScienceComputer Science (R0)