Online Signature Verification Method Based on the Acceleration Signals of Handwriting Samples

  • Horst Bunke
  • János Csirik
  • Zoltán Gingl
  • Erika Griechisch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


Here we present a method for online signature verification treated as a two-class pattern recognition problem. The method is based on the acceleration signals obtained from signing sessions using a special pen device. We applied a DTW (dynamic time warping) metric to measure any dissimilarity between the acceleration signals and represented our results in terms of a distance metric.


online signature biometrics signature verification 


  1. 1.
    Lei, H., Govindaraju, V.: A comparative study on the consistency of features in on-line signature verification. Pattern Rec. Letters 26, 2483–2489 (2005)CrossRefGoogle Scholar
  2. 2.
    Richiardi, J., Ketabdar, H., Drygajlo, A.: Local and global feature selection for on-line signature verification. In: Proc. IAPR 8th International Conference on Document Analysis and Recognition (ICDAR 2005), pp. 625–629 (2005)Google Scholar
  3. 3.
    Nanni, L., Maiorana, E., Lumini, A., Campisi, P.: Combining local, regional and global matchers for a template protected on-line signature verification system. Expert Syst. Appl. 37, 3676–3684 (2010)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification - the state of the art. Pattern Rec. 22(2), 107–131 (1989)CrossRefGoogle Scholar
  6. 6.
    Leclerc, F., Plamondon, R.: Automatic Signature Verification. In: Progress in Automatic Signature VerificationGoogle Scholar
  7. 7.
    Daramola, S., Ibiyemi, T.: An efficient on-line signature verification system. International Journal of Engineering and Technology 10(4) (2010)Google Scholar
  8. 8.
    Kholmatov, A., Yanikoglu, B.: Identity authentication using an improved online signature verification method. Pattern Rec. Letters 26, 2400–2408 (2005)CrossRefGoogle Scholar
  9. 9.
    Fang, P., Wu, Z., Shen, F., Ge, Y., Fang, B.: Improved dtw algorithm for online signature verification based on writing forces. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 631–640. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Mailah, M., Lim, B.H.: Biometric signature verification using pen position, time, velocity and pressure parameters. Jurnal Teknologi A 48A, 35–54 (2008)Google Scholar
  11. 11.
    Baron, R., Plamondon, R.: Acceleration measurement with an instrumented pen for signature verification and handwriting analysis. IEEE Transactions on Instrumentation and Measurement 38, 1132–1138 (1989)CrossRefGoogle Scholar
  12. 12.
    Lew, J.S.: Optimal accelerometer layouts for data recovery in signature verification. IBM J. Res. Dev. 24, 496–511 (1980)CrossRefzbMATHGoogle Scholar
  13. 13.
    Bashir, M., Kempf, J.: Reduced dynamic time warping for handwriting recognition based on multi-dimensional time series of a novel pen device. World Academy of Science, Engineering and Technology 45, 382–388 (2008)Google Scholar
  14. 14.
    Rohlik, O., Mautner, P., Matousek, V., Kempf, J.: A new approach to signature verification: digital data acquisition pen. Neural Network World 11(5), 493–501 (2001)Google Scholar
  15. 15.
    Mautner, P., Rohlik, O., Matousek, V., Kempf, J.: Signature verification using art-2 neural network. In: Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002, vol. 2, pp. 636–639 (November 2002)Google Scholar
  16. 16.
    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., Moro, Q.I.: MCYT baseline corpus: a bimodal biometric database. IEE Proceedings of Vision, Image and Signal Processing 150(6), 395–401 (2003)CrossRefGoogle Scholar
  17. 17.
    Kopasz, K., Makra, P., Gingl, Z.: Edaq530: A transparent, open-end and open-source measurement solution in natural science education. Eur. J. Phys. 32, 491–504 (2011)Google Scholar
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Horst Bunke
    • 1
  • János Csirik
    • 2
  • Zoltán Gingl
    • 2
  • Erika Griechisch
    • 2
  1. 1.Institute of Informatics and Applied MathematicsBernSwitzerland
  2. 2.Department of InformaticsUniversity of SzegedSzegedHungary

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