Two Bioinspired Methods for Dynamic Signatures Analysis

  • Jânio Canuto
  • Bernadette Dorizzi
  • Jugurta Montalvão
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)

Abstract

This work focuses on the problem of dynamic signature segmentation and representation. A brief review of segmentation techniques for online signatures and movement modelling is provided. Two dynamic signature segmentation/representation methods are proposed. These methods are based on psychophysical evidences that led to the well-known Minimum Jerk Model. These methods are alternatives to the existing techniques and are very simple to implement. Experimental evidence indicates that the Minimum Jerk is in fact a good choice for signature representation amongst the family of quadratic derivative cost functions defined in Section 2.

Keywords

Dynamic Signatures Segmentation Minimum Jerk 

References

  1. 1.
    Impedovo, D., Pirlo, G.: Automatic Signature Verification: The State of the Art. IEEE Trans. Syst., Man, and Cybern. – Part C: App. and Rev. 38(5), 609–635 (2008)CrossRefGoogle Scholar
  2. 2.
    Yue, K.W., Wijesoma, W.S.: Improved Segmentation and Segment Association for Online Signature Verification. In: Proc. IEEE Int. Conf. Syst., Man, Cybern., vol. 4, pp. 2752–2756 (2000)Google Scholar
  3. 3.
    Dolfing, J.G.A., Aarts, E.H.L., van Oosterhout, J.J.G.M.: On-line Signature Verification with Hidden Markov Models. In: Proc. 4th Int. Conf. Pat. Rec., vol. 2, pp. 1309–1312 (1998)Google Scholar
  4. 4.
    Plamondon, R., O’Reilly, C., Galbally, J., Almaksour, A., Anquetil, E.: Recent developments in the study of rapid human movements with the kinematic theory: Applications to handwriting and signature synthesis. Pat. Rec. Letters (June 15, 2012) (in press)Google Scholar
  5. 5.
    Lee, J., Yoon, H.-S., Soh, J., Chun, B.T., Chung, Y.K.: Using geometric extrema for segment-to-segment characteristics comparison in online signature verification. Pat. Rec. 37, 93–103 (2004)CrossRefMATHGoogle Scholar
  6. 6.
    Qu, T., Saddik, A.E., Adler, A.: A stroke based algorithm for dynamic signature verification. In: Proc. Can. Conf. Ele. Comp. Eng. (CCECE), pp. 461–464 (2004)Google Scholar
  7. 7.
    Berret, B.: Integration de la force gravitaire dans la planification motrice et le controle des mouvements du bras et du corps. PhD Thesis, Pozzo, T., Gauthier, J.-P. (advisors), Bougogne Univesity (2008) Google Scholar
  8. 8.
    Richardson, M.J.E., Flash, T.: Comparing Smooth Arm Movements with the Two-Thirds Power Law and the Related Segmented-Control Hypothesis. J. Neuroscience 22(18), 8201–8211 (2002)Google Scholar
  9. 9.
    Dijoua, M., Plamondon, R.: The Limit Profile of a Rapid Movement Velocity. Human Movement Science 29(1), 48–61 (2010)CrossRefGoogle Scholar
  10. 10.
    Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuv, M., Espinosa, V., Satue, A., Hermanez, I., Igarza, J.J., Vivaracho, C., Escudero, D., Moro, Q.I.: MCYT baseline corpus: a bimodal biometric database. IEEE Proc. Vis., Im., Sig. Proc. 150(6), 395–401 (2003)CrossRefGoogle Scholar
  11. 11.
    Canuto, J., Dorizzi, B., Montalvão, J.: Dynamic Signatures Representation Using the Minimum Jerk Principle. In: Proc. 4th IEEE Biosig. Biorob. Conf. (ISSNIP), pp. 1–6 (2013)Google Scholar
  12. 12.
    Ziv, J.: Coding Theorems for Individual Sequences. IEEE Trans. Inf. Theory IT-24(4), 405–412 (1978)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jânio Canuto
    • 1
  • Bernadette Dorizzi
    • 1
  • Jugurta Montalvão
    • 2
  1. 1.Institut Mines-Telecom, CNRS UMR5157 SAMOVARTelecom SudParisÉvryFrance
  2. 2.Electrical Engineering Department (DEL)Federal University of Sergipe (UFS)São CristóvãoBrazil

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