Dynamic Signature Recognition Based on Fisher Discriminant

  • Teodoro Schmidt
  • Vladimir Riffo
  • Domingo Mery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

Biometric technologies are the primary tools for certifying identity of individuals. But cost of sensing hardware plus degree of physical invasion required to obtain reasonable success are considered major drawbacks. Nevertheless, the signature is generally accepted as one means of identification. We present an approach on signature recognition using face recognition algorithms to obtain class descriptors and then use a simple classifier to recognize signatures. We also present an algorithm to store the writing direction of a signature, applying a linear transformation to encode this data as a gray scale pattern into the image. The signatures are processed applying Principal Components Analysis and Linear Discriminant Analysis creating descriptors that can be identified using a KNN classifier. Results revealed an accuracy performance rate of 97.47% under cross-validation over binary images and an improvement of 98.60% of accuracy by encoding simulated dynamic parameters. The encoding of real dynamic data boosted the performance rate from 90.21% to 94.70% showing that this technique can be a serious contender to other signature recognition methods.

Keywords

signature recognition on-line signatures off-line signatures fishersignatures 

References

  1. 1.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14, 4–20 (2004)CrossRefGoogle Scholar
  2. 2.
    Prabhakar, S., Pankanti, S., Jain, A.K.: Biometric recognition: security and privacy concerns. IEEE Security & Privacy 1, 33–42 (2003)CrossRefGoogle Scholar
  3. 3.
    Faundez-Zanuy, M.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40, 981–992 (2007)CrossRefMATHGoogle Scholar
  4. 4.
    Pascual-Gaspar, J.M., Faundez-Zanuy, M., Vivaracho, C.: Fast on-line signature recognition based on VQ with time modeling. Engineering Applications of Artificial Intelligence 24(2), 368–377 (2011)CrossRefGoogle Scholar
  5. 5.
    Pascual-Gaspar, J.M., Faundez-Zanuy, M., Vivaracho, C.: Efficient on-line signature recognition based on multi-section vector quantization. Pattern Analysis & Applications 14(1), 37–45 (2010)MathSciNetGoogle Scholar
  6. 6.
    Karouni, A., Daya, B., Bahlak, S.: Offline signature recognition using neural networks approach. In: Procedia Computer Science, World Conference on Information Technology, vol. 3, pp. 155–161 (2011)Google Scholar
  7. 7.
    Al-Mayyan, W., Own, H.S., Zedan, H.: Rough set approach to online signature identification. Digital Signal Processing 21(3), 477–485 (2011)CrossRefGoogle Scholar
  8. 8.
    Radhika, K.R., Venkatesha, M.K., Sekhar, G.N.: Signature authentication based on subpattern analysis. Applied Soft Computing 11(3), 3218–3228 (2011)CrossRefGoogle Scholar
  9. 9.
    Ebrahimpour, R, Amiri, A., Nazari, M., Hajiany, A.: Robust Model for Signature Recognition Based on Biological Inspired Features. International Journal of Computer and Electrical Engineering 2(4) (August 2010)Google Scholar
  10. 10.
    Meshoul, S., Batouche, M.: A novel approach for online signature verification using fisher based probabilistic neural networks. In: Proceedings - IEEE Symposium on Computers and Communications, pp. 314–319 (2010)Google Scholar
  11. 11.
    Tu, J.V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology 49(11), 1225–1231 (1996)CrossRefGoogle Scholar
  12. 12.
    Kulkarni, V.B.: A Colour Code Algorithm for Signature Recognition. Electronic Letters on Computer Vision and Image Analysis 6, 1–12 (2007)Google Scholar
  13. 13.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  14. 14.
    Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proceedings of Computer Vision & Pattern Recognition, CVPR 1991, IEEE Computer Society Conference, pp. 586–591 (1991)Google Scholar
  15. 15.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  16. 16.
    Vivaracho-Pascual, C., Faundez-Zanuy, M., Pascual, J.M.: An efficient low cost approach for on-line signature recognition based on length normalization and fractional distances. Pattern Recognition 42, 183–193 (2009)CrossRefMATHGoogle Scholar
  17. 17.
    Erkmen, B., Kahraman, N., Vural, R., Yildirim, T.: CSFNN optimization of signature recognition problem for a special VLSI NN chip. In: 3rd International Symposium on Communications, Control and Signal Processing, ISCCSP 2008, pp. 1082–1085 (2008)Google Scholar
  18. 18.
    Riffo, V., Schmidt, T., Mery, D.: Propuesta Novedosa de Reconocimiento Dinmico de Firmas. In: Proceeding of First Chilean Workshop on Pattern Recognition: Theory and Applications, pp. 44–51 (2009)Google Scholar
  19. 19.
    Hari, V.: Accelerating the SVD Block-Jacobi Method. Computing 75, 27–53 (2005)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Blumenstein, M., Ferrer Miguel, A., Vargas, J.F.: The 4NSigComp2010 off-line signature verification competition: Scenario 2. In: Proceedings of 12th International Conference on Frontiers in Handwriting Recognition, Kolkata, India, November 16-18, pp. 721–726 (2010) ISSBN: 978-0-7695-4221-8Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Teodoro Schmidt
    • 1
  • Vladimir Riffo
    • 1
  • Domingo Mery
    • 1
  1. 1.Pontificia Universidad Catolica de Chile, PUCChile

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