Abstract
Dynamic signature is a very interesting biometric attribute which is commonly socially acceptable. In this paper we propose a new method for the dynamic signature verification using stable partitions of the signature. This method assumes selection of two the most stable hybrid partitions individually for the signer. Hybrid partitions are formed by a combination of vertical and horizontal sections of the signature. The selected partitions are used during identity verification process. In the test of the proposed method we used BioSecure DS2 database, distributed by the BioSecure Association.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Bilski, J.: Momentum modification of the RLS algorithms. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 151–157. Springer, Heidelberg (2004)
Bilski, J., Rutkowski, L.: Numerically robust learning algorithms for feed forward neural networks. Advances in Soft Computing, pp. 149–154 (2003)
Bilski, J., Smoląg, J.: Parallel realisation of the recurrent RTRN neural network learning. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 11–16. Springer, Heidelberg (2008)
Bilski, J., Smoląg, J.: Parallel Realisation of the Recurrent Elman Neural Network Learning. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS (LNAI), vol. 6114, pp. 19–25. Springer, Heidelberg (2010)
Bilski, J., Smoląg, J.: Parallel Realisation of the Recurrent Multi Layer Perceptron Learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part I. LNCS, vol. 7267, pp. 12–20. Springer, Heidelberg (2012)
Bilski, J., Smoląg, J.: Parallel approach to learning of the recurrent jordan neural network. 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. 32–40. Springer, Heidelberg (2013)
Bilski, J., Smoląg, J.: Parallel architectures for learning the RTRN and Elman dynamic neural networks, IEEE Trans. Parallel and Distributed Systems PP(99) (2014)
Bilski, J., Smoląg, J., Galushkin, A.I.: The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks. 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. 12–21. Springer, Heidelberg (2014)
Bilski, J., Litwiński, S., Smoląg, J.: Parallel realisation of QR algorithm for neural networks learning. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 158–165. Springer, Heidelberg (2004)
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)
Batista, L., Granger, E., Sabourin, R.: Dynamic selection of generative discriminative ensembles for off-line signature verification. Pattern Recognition 45, 1326–1340 (2012)
Bhattacharya, I., Ghosh, P., Biswas, S.: Offline Signature Verification Using Pixel Matching Technique. Procedia Technology 10, 970–977 (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)
Faúndez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40, 981–992 (2007)
Faúndez-Zanuy, M., Pascual-Gaspar, J.M.: Efficient on-line signature recognition based on multi-section vector quantization. Formal Pattern Analysis & Applications 14, 37–45 (2011)
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.: 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)
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)
Gakowski, T., Rutkowski, L.: Nonparametric fitting of multivariate functions. IEEE Trans. Automatic Control AC-31(8), 785–787 (1986)
Greblicki, W., Rutkowska, D., Rutkowski, L.: An orthogonal series estimate of time-varying regression. Annals of the Institute of Statistical Mathematics 35(2), 215–228 (1983)
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)
Homepage of Association BioSecure, http://biosecure.it-sudparis.eu (accessed: December 16, 2014)
Houmani, N., Garcia-Salicetti, S., Mayoue, A., Dorizzi, B.: BioSecure Signature Evaluation Campaign 2009 (BSEC 2009): Results (2009)
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, 2817–2832 (2010)
Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognition 44, 2231–2240 (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)
Kroll, A.: On choosing the fuzziness parameter for identifying TS models with multidimensional membership functions. Journal of Artificial Intelligence and Soft Computing Research 1(4), 283–300 (2011)
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.: A novel hybrid-maximum neural network in stereo-matching process. Neural Computing and Applications 23, 2435–2450 (2013)
Laskowski, Ł., Jelonkiewicz, J.: Self-correcting neural network for stereo-matching problem solving. Lecture Notes in Computer Science, vol. 138, pp. 1–26. Springer (2015)
Lumini, A., Nanni, L.: Ensemble of on-line signature matchers based on overcomplete feature generation. Expert Systems with Applications 36, 5291–5296 (2009)
Maiorana, E.: Biometric cryptosystem using function based on-line signature recognition. Expert Systems with Applications 37, 3454–3461 (2010)
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.: Advanced methods for two-class problem formulation for on-line signature verification. Neurocomputing 69, 854–857 (2006)
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)
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., Rutkowski, L., Scherer, R.: A method for learning of hierarchical fuzzy systems. In: Intelligent Technologies - Theory and Applications, pp. 124–129 (2002)
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)
Pławiak, P., Tadeusiewicz, R.: Approximation of phenol concentration using novel hybrid computational intelligence methods. Applied Mathematics and Computer Science 24(1) (2014)
Pabiasz, S., Starczewski, J.T.: A new approach to determine three-dimensional facial landmarks. 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. 286–296. Springer, Heidelberg (2013)
Pabiasz, S., Starczewski, J.T., Marvuglia, A.: A new three-dimensional facial landmarks in recognition. 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. 179–186. Springer, Heidelberg (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. Przegląd Elektrotechniczny 86(2), 342–346 (2010)
Rutkowski, L.: Nonparametric identification of quasi-stationary systems. Systems & Control Letters 6(1), 33–35 (1985)
Rutkowski, L.: Real-time identification of time-varying systems by non-parametric algorithms based on Parzen kernels. Int. Journal of Systems Science 16(9), 1123–1130 (1985)
Rutkowski, L.: A general-approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Trans. Circuits and Systems 33(8), 812–818 (1986)
Rutkowski, L.: Computational Intelligence. Springer, Heidelberg (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, P., Vascak, J., Kvasnicka, V., Pospichal, J. (eds.) Intelligent Technologies - Theory and Applications, vol. 76, pp. 85–90. IOS Press (2002)
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, vol. 6114, pp. 645–650. Springer, Heidelberg (2010)
Starczewski, J.T., Scherer, R., Korytkowski, M., Nowicki, R.: Modular type-2 neuro-fuzzy systems. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds.) PPAM 2007. LNCS, vol. 4967, pp. 570–578. Springer, Heidelberg (2008)
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 (LNAI), 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)
Woźniak, M., Kempa, W.M., Gabryel, M., Nowicki, R.: A finite-buffer queue with single vacation policy-analytical study with evolutionary positioning. Int. Journal of Applied Mathematics and Computer Science 24, 887–900 (2014)
Woźniak, M., Kempa, W.M., Gabryel, M., Nowicki, R.K., Shao, Z.: On applying evolutionary computation methods to optimization of vacation cycle costs in finite-buffer queue. 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. 480–491. Springer, Heidelberg (2014)
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)
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.: 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., 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 (LNAI), vol. 8468, pp. 216–230. 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., Er, M.J. (2015). 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., 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_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-19369-4_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19368-7
Online ISBN: 978-3-319-19369-4
eBook Packages: Computer ScienceComputer Science (R0)