Encyclopedia of Biometrics

2015 Edition
| Editors: Stan Z. Li, Anil K. Jain

Signature Matching

  • Marcos Martinez-Diaz
  • Julian Fierrez
  • Seiichiro Hangai
Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7488-4_140


Signature similarity computation


The objective of signature matching techniques is to compute the similarity between a given signature and a signature model or reference signature set. Several pattern recognition techniques have been proposed as matching algorithms for signature recognition. In online signature verification systems, signature matching algorithms have followed two main approaches. Feature-based algorithms usually compute the similarity among multidimensional feature vectors extracted from the signature data with statistical classification techniques. On the other hand, function-based algorithms perform matching by computing the distance among time sequences extracted from the signature data with technique such as hidden Markov models and dynamic time warping. Off-line signature matching has followed many different approaches, most of which are related to image processing and shape recognition.

This entry focuses on online signature matching, although...

This is a preview of subscription content, log in to check access.


  1. 1.
    W. Nelson, W. Turin, T. Hastie, Statistical methods for on-line signature verification. Int. J. Pattern Recogn. Artif. Intell. 8(3), 749–770 (1994)Google Scholar
  2. 2.
    L.L. Lee, T. Berger, E. Aviczer, Reliable on-line human signature verification systems. IEEE Trans. Pattern Anal. Mach. Intell. 18(6), 643–647 (1996)Google Scholar
  3. 3.
    M. Martinez-Diaz, J. Fierrez, J. Ortega-Garcia, Universal background models for dynamic signature verification, in Proceedings IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS, Washington, DC, 2007, pp. 1–6Google Scholar
  4. 4.
    J. Fierrez-Aguilar, L. Nanni, J. Lopez-Penalba, J. Ortega-Garcia, D. Maltoni, An on-line signature verification system based on fusion of local and global information, in Proceedings of IAPR International Conference on Audio- and Video-Based Biometric Person Authentication, AVBPA, Hilton Rye Town. LNCS, vol. 3546 (Springer, 2005), pp. 523–532Google Scholar
  5. 5.
    Y. Sato, K. Kogure, Online signature verification based on shape, motion and writing pressure, in Proceedings of sixth International Conference on Pattern Recognition, Munich, 1982, pp. 823–826Google Scholar
  6. 6.
    R. Martens, L. Claesen, Dynamic programming optimisation for on-line signature verification, in Proceedings fourth International Conference on Document Analysis and Recognition, ICDAR, Ulm, vol. 2, 1997, pp. 653–656Google Scholar
  7. 7.
    A. Kholmatov, B. Yanikoglu, Identity authentication using improved online signature verification method. Pattern Recogn. Lett. 26(15), 2400–2408 (2005)Google Scholar
  8. 8.
    J. Fierrez, D. Ramos-Castro, J. Ortega-Garcia, J. Gonzalez-Rodriguez, HMM-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn. Lett. 28(16), 2325–2334 (2007)Google Scholar
  9. 9.
    J.G.A. Dolfing, E.H.L. Aarts, J.J.G.M. van Oosterhout, On-line signature verification with hidden Markov models, in Proceedings of the International Conference on Pattern Recognition, ICPR, Brisbane (IEEE CS Press 1998), pp. 1309–1312Google Scholar
  10. 10.
    B.L. Van, S. Garcia-Salicetti, B. Dorizzi, On using the Viterbi path along with HMM likelihood information for online signature verification. IEEE Trans. Syst. Man Cybern. B 37(5), 1237–1247 (2007)Google Scholar
  11. 11.
    L. Yang, B.K. Widjaja, R. Prasad, Application of hidden Markov models for signature verification. Pattern Recogn. 28(2), 161–170 (1995)Google Scholar
  12. 12.
    H. Sakoe, S. Chiba, Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. 26, 43–49 (1978)zbMATHGoogle Scholar
  13. 13.
    L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)Google Scholar
  14. 14.
    J. Richiardi, A. Drygajlo, Gaussian mixture models for on-line signature verification, in Proceedings of ACM SIGMM Workshop on Biometric Methods and Applications, WBMA, Berkeley, 2003, pp. 115–122Google Scholar
  15. 15.
    D. Impedovo, G. Pirlo, Automatic signature verification: the state of the art. IEEE Trans. Syst. Man. Cybern. C Appl. Rev. 38(5), 609–635 (2008)Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Marcos Martinez-Diaz
    • 1
  • Julian Fierrez
    • 2
  • Seiichiro Hangai
    • 3
  1. 1.Biometric Recognition Group – ATVS, Escuela Politecnica SuperiorUniversidad Autonoma de Madrid, Campus de CantoblancoMadridSpain
  2. 2.Universidad Autonoma de MadridMadridSpain
  3. 3.Department of Electrical EngineeringTokyo University of ScienceTokyoJapan