Skip to main content

Advertisement

SpringerLink
Log in

Online signature verification based on string edit distance

  • Original Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature verification systems has significantly improved in the last decade, making it possible to rely on machines in particular cases or to support human experts. Yet, based on only few genuine references, signature verification is still a challenging task. The present paper provides a comprehensive comparison of two prominent string matching algorithms that can be readily used for signature verification. Moreover, it evaluates a recent cost model for string matching which turns out to be particularly well suited for the task of signature verification. On three benchmarking data sets, we show that this model outperforms the two standard models for string matching with statistical significance.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Multimodal systems combine algorithms from different categories.

  2. Often the DTW algorithm is endowed with a Sakoe–Chiba band [24] in order to exclude unusual warping paths and speed up the computation.

  3. The Euclidean distance could be replaced by any other Minkowski metric

  4. We perform our evaluation on a 3.5 GHz Intel Core i7 with 16 GB RAM.

References

  1. Ansari, A., Hanmandlu, M., Kour, J., Singh, A.: Online signature verification using segment-level fuzzy modelling. IET Biom. 3(3), 113–127 (2014)

    Article  Google Scholar 

  2. Bansal, M., Hanmandlu, M., Kumar, P.: Iris based authentication using local principal independent components. Optik-Int. J. Light Electron Opt. 127(11), 4808–4814 (2016)

    Article  Google Scholar 

  3. Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzales-Rodriguez, J.: Hmm-based on-line signature verification: feature extraction and signature modeling. Pattern Recognit. Lett. 28(16), 2325–2334 (2007)

    Article  Google Scholar 

  4. Guru, D., Prakash, H.: Online signature verification and recognition: an approach based on symbolic representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1059—1073 (2009)

  5. Ibrahim, M., Kyan, M., Guang, L.: On-line signature verification using global features. In: 2009 Canadian Conference on Electrical and Computer Engineering (2009)

  6. Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art’. IEEE Trans. Syst. Man Cybern. 38(5), 609–635 (2008)

    Article  Google Scholar 

  7. Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognit. Lett. 26(15), 1400–2408 (2005)

    Article  Google Scholar 

  8. Kholmatov, A., Yanikoglu, B.: SUSIG: an on-line signature database, associated protocols and benchmark results. Pattern Anal. Appl. 12(3), 227–236 (2009)

    Article  MathSciNet  Google Scholar 

  9. Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  10. Liwicki, M., Blumenstein, M., van den Heuvel, E., Berger, C., Stoel, R.D., Found, B., Chen, X., Malik, M.I.: Sigcomp11: Signature verification competition for on- and offline skilled forgeries. In: Proc. 11th Int. Conference on Document Analysis and Recognition (2011)

  11. Martinez-Diaz, M., Fierrez, J., Krish, R., Galbally, J.: Mobile signature verification: feature robustness and performance comparison. IET Biom. 3(4), 267–277 (2014)

    Article  Google Scholar 

  12. Mehdi, S., Arakala, A., Davis, S., Horadam, K.: Retina verification system based on biometric graph matching. IEEE Trans. Image Process. 22(9), 3625–3635 (2013)

    Article  Google Scholar 

  13. Mehdi Lajevardi, S., Arakala, A., Davis, S., Horadam, K.: Hand vein authentication using biometric graph matching. IET Biom. 3(4), 302–313 (2014)

    Article  Google Scholar 

  14. Nanni, L., Lumini, A.: Ensemble of parzen window classifiers for on-line signature verification. Neurocomputing 68, 217–224 (2005)

    Article  Google Scholar 

  15. O’Gorman, L.: Comparing passwords, tokens, and biometrics for user authentication. Proc. IEEE 91(12), 2021–2040 (2003)

    Article  Google Scholar 

  16. Oka, M., Kato, K., Xu, Y., Liang, L., Wen, F.: Scribble-a-secret: Similarity-based password authentication using sketches. In: In Proc. of the 19th International Conference on Pattern Recognition, pp. 1–4 (2008)

  17. Ortega-Garcia J. andFierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez, M., Espinosa, V., Satue A. andHernaez, I., Igarza, J.J., Vivaracho, C., Escudero, D., Moro, Q.I.: Mcyt baseline corpus: A bimodal biometric database. IEEE Proc. Vis. Image Signal Process. Spec. Issue Biom. Internet 150(6), 395–401 (2003)

  18. Plamondon, R., Pirlo, G., Impedovo, D.: On-line Signature Verification, chap. 27, pp. 917–947. Springer (2014)

  19. Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. PAMI 22(1), 63–84 (2000)

    Article  Google Scholar 

  20. Riesen, K., Hanne, T., Schmidt, R.: Sketch-based user authentication with a novel string edit distance model. IEEE Trans. Syst. Man Cybern. Syst. PP(99) (2016)

  21. Roth, J., Liu, X., Metaxas, D.: On continuous user authentication via typing behavior. IEEE Trans. Image Process. 23(10), 4611–4624 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  22. Sae-Bae, N., Memon, N.: Online signature verification on mobile devices. IEEE Trans. Inf. Forensics Secur. 9(6), 933—947 (2014)

  23. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)

  24. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)

    Article  MATH  Google Scholar 

  25. Stajano, F.: No more passwords. In: In Proc. Security Protocols Workshop, pp. 49–81 (2011)

  26. Ukkonen, E.: Algorithms for approximate string matching. Inf. Control 64(1–3), 100–118 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  27. 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 Recognit. 42(1), 183–193 (2009)

    Article  MATH  Google Scholar 

  28. Wong, W., Teoh, A., Kho, Y., Wong, M.: Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template. Pattern Recognit. 51, 197–208 (2016)

    Article  Google Scholar 

  29. Yanikoglu, B., Kholmatov, A.: Online signature verification using fourier descriptors. Eur. J. Adv. Signal Process. (1–14) (2009)

Download references

Acknowledgements

We would like to thank Prof. Dr. Andreas Fischer for his valuable comments and help on the DTW reference system.

Author information

Authors and Affiliations

  1. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600, Olten, Switzerland

    Kaspar Riesen

  2. Intelligent Insights GmbH, Liebefeldstrasse 6, 3097, Liebefeld, Switzerland

    Roman Schmidt

Authors
  1. Kaspar Riesen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kaspar Riesen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work has been supported by the Commission for Technology and Innovation (CTI) project Nr. 18029.1 PFES-ES and the Bern Economic Development Agency.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Riesen, K., Schmidt, R. Online signature verification based on string edit distance. IJDAR 22, 41–54 (2019). https://doi.org/10.1007/s10032-019-00316-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10032-019-00316-1

Keywords

Search

Navigation