Signature verification: A comprehensive study of the hidden signature method

Joanna Putz-Leszczyńska

International Journal of Applied Mathematics and Computer Science, Tome 25 (2015), p. 659-674 / Harvested from The Polish Digital Mathematics Library

Résumé

Many handwritten signature verification algorithms have been developed in order to distinguish between genuine signatures and forgeries. An important group of these methods is based on dynamic time warping (DTW). Traditional use of DTW for signature verification consists in forming a misalignment score between the verified signature and a set of template signatures. The right selection of template signatures has a big impact on that verification. In this article, we describe our proposition for replacing the template signatures with the hidden signature-an artificial signature which is created by minimizing the mean misalignment between itself and the signatures from the enrollment set. We present a few hidden signature estimation methods together with their comprehensive comparison. The hidden signature opens a number of new possibilities for signature analysis. We apply statistical properties of the hidden signature to normalize the error signal of the verified signature and to use the misalignment on the normalized errors as a verification basis. A result, we achieve satisfying error rates that allow creating an on-line system, ready for operating in a real-world environment.

Détail

Publié le : 2015-01-01

Zbl 1322.68169

EUDML-ID : urn:eudml:doc:271786

Bibliographie

[000] Davis, L. (1991). Handbook of Genetic Algorithms, Vol. 115, Van Nostrand Reinhold, New York, NY.

[001] Durrett, R. (2010). Probability: Theory and Examples, Cambridge University Press, Cambridge. | Zbl 1202.60001

[002] Faundez-Zanuy, M. (2007). On-line signature recognition based on VQ-DTW, Pattern Recognition 40(3): 981-992. | Zbl 1119.68172

[003] Fauziyah, S., Azlina, O., Mardiana, B., Zahariah, A.M. and Haroon, H. (2009). Signature verification system using support vector machine, MASAUM Journal of Basic and Applied Sciences 1(2): 291-294.

[004] Galbally, J., Fierrez, J., Freire, M. and Ortega-Garcia, J. (2007). Feature selection based on genetic algorithms for on-line signature verification, IEEE Workshop on Automatic Identification Advanced Technologies, Alghero, Italy, pp. 198-203.

[005] Guru, D. and Prakash, H. (2007). Symbolic representation of on-line signatures, International Conference on Computational Intelligence and Multimedia Applications 2007, Sivakasi, Tamil Nadu, India, pp. 313-317.

[006] Herbst, N. and Liu, C. (1977). Automatic signature verification based on accelerometry, IBM Journal of Research and Development 21(3): 245-253.

[007] Hong-Wei, J. and Zhong-Hua, Q. (2005). Signature Verification Using Wavelet Transform and Support Vector Machine, Springer, Berlin/Heidelberg.

[008] Impedovo, D. and Pirlo, G. (2008). Automatic signature verification: The state of the art, IEEE Transactions on Systems, Man, And Cybernetics, Part C: Applications and Reviews 35(5): 609-635.

[009] Kam, M., Gummadidala, K., Fielding, G. and Conn, R. (2001). Signature authentication by forensic document examiners, Journal of Forensic Sciences 46(4): 884-888.

[010] Kholmatov, A. and Yanikoglu, B. (2005). Identity authentication using improved online signature verification method, Pattern Recognition Letters 26(15): 2400-2408.

[011] Marinai, S., Gori, M. and Soda, G. (2005). Artificial neural networks for document analysis and recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence 27(1): 23-35.

[012] Miguel-Hurtado, O., Mengibar-Pozo, L., Lorenz, M. and Liu-Jimenez, J. (2007). On-line signature verification by dynamic time warping and Gaussian mixture models, 41st Annual IEEE International Carnahan Conference on Security Technology, Ottawa, Canada, pp. 23-29.

[013] Muramatsu, D. and Matsumoto, T. (2003). An HMM online signature verifier incorporating signature trajectories, 7th International Conference on Document Analysis and Recognition, Washington, DC, USA, Vol. 1, pp. 438-442.

[014] Nanni, L. and Lumini, A. (2008). A novel local on-line signature verification system, Pattern Recognition Letters 29(5): 559-568.

[015] 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. and Moro, Q.-I. (2003). MCYT baseline corpus: A bimodal biometric database, IEE Proceedings: Vision, Image and Signal Processing 150(6): 395-401.

[016] Pascual-Gaspar, J.M., Cardenoso-Payo, V. and Vivaracho-Pascual, C.E. (2009). Practical on-line signature verification, in M. Tistarelli and M.S. Nixon (Eds.), Advances in Biometrics, Lecture Notes in Computer Science, Vol. 5558, Springer, Berlin/Heidelberg, pp. 1180-1189.

[017] Putz-Leszczynska, J. and Pacut, A. (2005). Dynamic time warping in subspaces for on-line signature verification, Advances in Graphonomics: Proceedings of IGS 2005, Salerno, Italy, pp. 108-112.

[018] Putz-Leszczynska, J. and Pacut, A. (2009). Model approach to DTW signature verification using error signals, Advances in Graphonomics: Procedings of IGS 2009, Dijon, France, pp. 166-169.

[019] Putz-Leszczynska, J. and Pacut, A. (2013). Universal forgery features idea: A solution for user-adjusted threshold in signature verification, Transactions on Computational Collective Intelligence 7770(9): 152-172.

[020] Quan, Z.-H., Huang, D.-S., Xia, X.-L., Lyu, M.R. and Lok, T.-M. (2006). Spectrum analysis based on windows with variable widths for online signature verification, Proceedings of the International Conference on Pattern Recognition (ICPR '06), Hong Kong, China, Vol. 2, pp. 1122-1125.

[021] Sakamoto, D., Morita, H., Ohishi, T., Komiya, Y. and Matsumoto, T. (2001). On-line signature verifier incorporating pen position, pen pressure and pen inclination trajectories, AVBPA 2001, Halmstad, Sweden, pp. 318-323. | Zbl 0980.68950

[022] Schmidt, C. and Kraiss, K.-F. (1997). Establishment of personalized templates for automatic signature verification, Proceedings of the 4th International Conference on Document Analysis and Recognition, Ulm, Germany, Vol. 1, pp. 263-267.

[023] Van, B., Garcia-Salicetti, S. and Dorizzi, B. (2007). On using the Viterbi path along with HMM likelihood information for online signature verification, IEEE Transactions on Systems, Man, and Cybernetics, Part B 37(5): 1237-1247.

[024] WACOM (2015). http://www.wacom.com.

[025] Xiao, X. and Leedham, G. (1999). Signature verification by neural networks with selective attention, Applied Intelligence 11(2): 213-223.

[026] Yoshimura, I. and Yoshimura, M. (1992). On-line signature verification incorporating the direction of pen movement. An experimental examination of the effectiveness, in S. Impedovo and J.C. Simon (Eds.), From Pixels to Features III: Frontiers in Handwriting Recognition, Elsevier, New York, NY, pp. 353-362.