Autoassociative Signature Authentication Based on Recurrent Neural Network

  • Jun RokuiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


In online handwriting authentication, it is difficult to forge handwriting because stroke characteristics cannot be reproduced using only a handwriting trajectory. However, it is difficult to completely reproduce registered stroke characteristics, even when signers attempt to reproduce their own signatures. For this reason, the principal criteria for authentication must be lowered. In this study, we use a recurrent neural network to model the behavior of the musculoskeletal function for handwriting. The proposed model can represent the handwriting stroke process for a character visualized by the authenticator. This research is an anti-counterfeit effort to reduce the error between autoassociative stroke information and handwriting stroke information.


Online handwriting authentication Recurrent neural network BPTT 



In this research, I would like to thank S. Sugawara for receiving great advice on data collection and RNN construction.


  1. 1.
    Muramatsu, D., Kondo, M., Sasaki, M., Tachibana, S., Matsumoto, T.: A Markov chain Monte Carlo algorithm for bayesian dynamic signature verification. IEEE Trans. Inf. Forensic Secur. 1(1), 22–34 (2006)CrossRefGoogle Scholar
  2. 2.
    Koishi, K., Kinoshita, S., Muramatsu, D., Matsumoto, T.: Online signature verification based on user-generic fusion model with Markov Chain Monte Carlo, taking into account user individuality. J. Adv. Comput. Intell. Intell. Inf. 13(4), 447–456 (2009)CrossRefGoogle Scholar
  3. 3.
    Tsukamoto, M., Kaneoya, T., Mano, W.: Verification of humans using the Musculo-Skeletal model with electromyograms. J. IEICE J J97–A(11), 672–682 (2014)Google Scholar
  4. 4.
    Kosslyn, S.: On cognitive neuroscience. J. Cogn. Neurosci. 6(3), 297–303 (1994)CrossRefGoogle Scholar
  5. 5.
    Marr, D.: A theory of cerebellar cortex. J Physiol. (Lond) 202, 437–470 (1969)CrossRefGoogle Scholar
  6. 6.
    Taguchi, H., Fujii, K.: A functional description of brain mechanics in the writing movement control. J. IEICE J70–D(3), 640–649 (1987)Google Scholar
  7. 7.
    Fukuda, O., Tsuji, T., Bu, N., Kaneko, M.: Pattern discrimination of time series EEG signals using a recurrent neural network. In: Proceedings of Artificial Intelligence and Soft Computing, pp. 450–455 (2002)Google Scholar
  8. 8.
    Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)CrossRefGoogle Scholar
  9. 9.
    Nakai, M., Akira, N., Shimodaira, H., Sagayama, S.: Substroke approach to HMM-based on-line Kanji handwriting recognition. In: Proceedings of Sixth International Conference on Document Analysis and Recognition, pp. 491–495 (2001)Google Scholar
  10. 10.
    Bahlmann, C., Burkhardt, H.: The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans. Pattern Anal. Mach. Intell. 26(3), 299–310 (2004)CrossRefGoogle Scholar
  11. 11.
    Pham, V., Bluche, T., Kermorvant, C., Louradour, J.: Dropout improves recurrent neural networks for handwriting recognition. In: Proceedings of 14th International Conference on Frontiers in Handwriting Recognition, pp. 285–290 (2014)Google Scholar
  12. 12.
    Wei, W., Guanglai, G.: Online handwriting Mongolia words recognition with recurrent neural networks. In: Proceedings of Fourth International Conference on Computer Sciences and Convergence Information Technology, pp. 165–167 (2009)Google Scholar
  13. 13.
    Goh, W.L., Mital, D.P., Babri, H.A.: An artificial neural network approach to handwriting recognition. In: Proceedings of Knowledge-Based Intelligent Electronic Systems vol.1, pp. 132–136 (1997)Google Scholar
  14. 14.
    Doetsch, P., Germany, A., Kozielski, M., Ney, H.: Fast and robust training of recurrent neural networks for offline handwriting recognition. In: Proceedings of 14th International Conference on Frontiers in Handwriting Recognition, pp. 279–284 (2014)Google Scholar
  15. 15.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematics and Computer Science Interdisciplinary, Faculty of Science and EngineeringShimane UniversityMatsueJapan

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