International Conference on Mining Intelligence and Knowledge Exploration

Mining Intelligence and Knowledge Exploration pp 58-69 | Cite as

Cluster Dependent Classifiers for Online Signature Verification

  • S. Manjunath
  • K.S. Manjunatha
  • D.S. Guru
  • M.T. Somashekara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)

Abstract

In this paper, the applicability of notion of cluster dependent classifier for online signature verification is investigated. For every writer, by the use of a number of training samples, a representative is selected based on minimum average distance criteria (centroid) across all the samples of that writer. Later k-means clustering algorithm is employed to cluster the writers based on the chosen representatives. To select a suitable classifier for a writer, the equal error rate (EER) is estimated using each of the classifier for every writer in a cluster. The classifier which gives the lowest EER for a writer is selected to be the suitable classifier for that writer. Once the classifier for each writer in a cluster is decided, the classifier which has been selected for a maximum number of writers in that cluster is decided to be the classifier for all writers of that cluster. During verification, the authenticity of the query signature is decided using the same classifier which has been selected for the cluster to which the claimed writer belongs. In comparison with the existing works on online signature verification, which use a common classifier for all writers during verification, our work is based on the usage of a classifier which is cluster dependent. On the other hand our intuition is to recommend to use a same classifier for all and only those writers who have some common characteristics and to use different classifiers for writers of different characteristics. To demonstrate the efficacy of our model, extensive experiments are carried out on the MCYT online signature dataset (DB1) consisting signatures of 100 individuals. The outcome of the experiments being indicative of increased performance with the adaption of cluster dependent classifier seems to open up a new avenue for further investigation on a reasonably large dataset.

Keywords

Writer representative Signature clustering Cluster dependent classifier Online signature verification 

Notes

Acknowledgement

The authors would like to thank J.F. Aguilar and J.O. Garcia for sharing MCYT-100, a sub corpus of online signature data set and thanks to Prof. Anil K. Jain for his associated support to get the dataset.

References

  1. 1.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification: the state of the art. Pattern Recogn. 2(2), 107–131 (1989)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Griess, F.D., Connell, S.D.: On-line signature verification. Pattern Recogn. 35(12), 2963–2972 (2002)MATHCrossRefGoogle Scholar
  3. 3.
    Impedovo, D., Pirlo, G.: Automatic signature verification: the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(5), 609–635 (2008)CrossRefGoogle Scholar
  4. 4.
    Zhang, Z., Wang, K., Wang, Y.: A survey of on-line signature verification. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 141–149. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Khan, M.K., Khan, M.A., Khan, U., Ahmad, I.: On-line signature verification by exploiting inter-feature dependencies. In: Proceedings of the ICPR, pp. 796–799 (2006)Google Scholar
  6. 6.
    Fierrez, J., Garcia, J.O., Ramos, D., Rodriguez, J.G.: HMM-based on-line signature verification: feature extraction and signature modeling. Pattern Recogn. Lett. 28(16), 2325–2334 (2007)CrossRefGoogle Scholar
  7. 7.
    Zou, J., Wang, Z.: Application of HMM to online signature verification based on segment differences. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds.) CCBR 2013. LNCS, vol. 8232, pp. 425–432. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Parodi, M., Gómez, J.C.: Legendre polynomials based feature extraction for online signature verification. Consistency analysis of feature combinations. Pattern Recogn. 47, 128–140 (2014)CrossRefGoogle Scholar
  9. 9.
    Meshoul, S., Batouche, M.: A novel approach for Online signature verification using fisher based probabilistic neural network. In: IEEE International Symposium on Computers and Communications (ISCC), pp. 314–319 (2010)Google Scholar
  10. 10.
    Muramatsu, M., Kondo, M., Sasaki, M., Tachibana, S., Matsumoto, T.: A markov chain monte carlo algorithm for bayesian dynamic signature verification. IEEE Trans. Inf. Forensics Secur. 1(1), 22–34 (2006)CrossRefGoogle Scholar
  11. 11.
    Guru, D.S., Prakash, H.N.: Online signature verification and recognition: An approach based on Symbolic representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1059–1073 (2009)CrossRefGoogle Scholar
  12. 12.
    Fiérrez-Aguilar, J., Nanni, L., Lopez-Peñalba, J., Ortega-Garcia, J., Maltoni, D.: An on-line signature verification system based on fusion of local and global information. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 523–532. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Nanni, L., Majorana, E., Lumini, A., Campisi, P.: Combining local, regional and global matchers for a template protected on-line signature verification system. Expert Syst. Appl. 37(5), 3676–3684 (2010)CrossRefGoogle Scholar
  14. 14.
    Nanni, L.: Experimental comparison of one-class classifiers for on-line signature verification. Neurocomputing 69(7–9), 869–873 (2006)CrossRefGoogle Scholar
  15. 15.
    Nanni, L., Lumini, A.: Advanced methods for two-class problem formulation for on-line signature verification. Neurocomputing 69, 854–857 (2006)CrossRefGoogle Scholar
  16. 16.
    Pirlo, G., Cuccovillo, V., Impedovo, D., Mignone, P.: On-line signature verification by multi-domain classification. In: 14th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 67–72 (2014)Google Scholar
  17. 17.
    Fiérrez-Aguilar, J., Krawczyk, S., Ortega-Garcia, J., Jain, A.K.: Fusion of local and regional approaches for on-line signature verification. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds.) IWBRS 2005. LNCS, vol. 3781, pp. 188–196. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Eskander, G.S., Sabourin, R., Granger, E.: Hybrid writer-independent–writer-dependent offline signature verification system. IET Biometrics 2(4), 169–181 (2013)CrossRefGoogle Scholar
  19. 19.
    Guru, D.S., Prakash, H.N., Manjunath, S.: On-line signature verification: an approach based on cluster representation of global features. In: International conference on Advances in Pattern Recognition (ICAPR), pp. 209–212 (2009)Google Scholar
  20. 20.
    Liu, N., Wang, Y.: Template selection for on-line signature verification. In: 19th International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar
  21. 21.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  22. 22.
    Houmani, N., Salicetti, S.G., Dorrizi, B.: On measuring forgery quality in online signatures. Pattern Recogn. 45(3), 1004–1018 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • S. Manjunath
    • 1
  • K.S. Manjunatha
    • 2
  • D.S. Guru
    • 2
  • M.T. Somashekara
    • 3
  1. 1.Department of Computer ScienceCentral University of KeralaKasargodIndia
  2. 2.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia
  3. 3.Department of Computer Science and ApplicationsBangalore UniversityBangaloreIndia

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