Online Signature Verification Based on Recursive Subset Training

  • D. S. Guru
  • K. S. Manjunatha
  • S. Manjunath
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)

Abstract

In this paper, a novel approach has been proposed for online signature verification based on recursive subset training. Our approach is based on estimating the Equal Error Rate (EER) of the entire system and then splitting the entire data set into two subsets based on the EER of the system. The two subsets includes writers whose individual EER is more than the EER of the system and writers whose EER is less than the EER of the system. This procedure is recursively repeated until writer level parameters are decided. Unlike other verification models where same features are used for all writers, our approach is based on identifying writer dependent features and also writer dependent thresholds. Initially, writer dependent features are selected using a suitable feature selection method. Signatures are clustered using Fuzzy C means and represented in the form of interval valued symbolic feature vector. Signature verification is done based on the selected representation and the EER of system is calculated. Once the EER of the system is estimated, our method is based on estimating the EER of individual writers and splitting the dataset into subsets and estimating the EER of each of the subset separately. This process of splitting the dataset into subset and treating each of the subsystem separately is repeated until the individual writer thresholds and features are identified. We conducted experiments on MCYT-DB1 to show the effectiveness of our novel approach.

Keywords

Subset recursive training writer dependent parameters Feature selection Fuzzy C means Symbolic feature vector 

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References

  1. 1.
    Argones, E.R., Luis, J.A.C.: Online signature verification based on Generative models. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 42, 1231–1241 (2012)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K., Griess, F.D., Connel, S.D.: On-line signature verification. Pattern Recognition 35, 2963–2972 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Nanni, L., Lumini, A.: A novel local on-line signature verification system. Pattern Recognition Letters 29, 559–568 (2008)CrossRefGoogle Scholar
  4. 4.
    Kashi, R., Hu, J., Nelson, W.L., Turin, W.: A Hidden Markov Model approach to on-line handwritten signature verification. International Journal of Document Analysis and Recognition (IJDAR) 1, 102–109 (1998)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification: the state of the art. Pattern Recognition 2(2), 63–94 (1989)Google Scholar
  7. 7.
    Guru, D.S., Prakash, H.N.: Online signature verification and recognition: An approach based on Symbolic representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 1059–1073 (2009)CrossRefGoogle Scholar
  8. 8.
    Feng, H., Wah, C.C.: Online Signature Verification using a new extreme points warping technique. Pattern Recognition Letters 24(16), 2943–2951 (2003)CrossRefGoogle Scholar
  9. 9.
    Faundez, Z., Zanuy, M.F.: On-line signature recognition based on VQ-DTW. Pattern Recognition 40(3), 981–992 (2007)CrossRefMATHGoogle Scholar
  10. 10.
    Fierrez, J., Ortega-Garcia, J., Ramos, D., Gonzalez-Rodriguez, J.: HMM-based on-line signature verification: Feature extraction and signature modeling. Pattern Recognition Letters 28(16), 2325–2334 (2007)CrossRefGoogle Scholar
  11. 11.
    Kholmatov, A., Yanikoglu, B.: Identity authentication using improved online signature verification method. Pattern Recognition Letters 26, 2400–2408 (2005)CrossRefGoogle Scholar
  12. 12.
    Parodi, M., Gomez’, J.C., Liwicki, M.: Online Signature Verification Based on Legendre Series Representation. Robustness Assessment of Different Feature Combinations. In: International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), pp. 715–723 (2012)Google Scholar
  13. 13.
    Bajaj, R., Chaudhury, S.: Signature verification using multiple neural classifier. Pattern Recognition Letters 30(1), 1–7 (1997)CrossRefGoogle Scholar
  14. 14.
    Baltzakis, H., Papamarkos, N.: A new signature verification technique based on a two stage neural classifier. Engineering Application of Artificial Intelligence 14, 95–103 (2001)CrossRefGoogle Scholar
  15. 15.
    Chen, Y., Ding, X.: Online signature verification using direction sequence string matching. In: Proceedings of SPIE, vol. 4875, pp. 744–749 (2002)Google Scholar
  16. 16.
    Wang, K., Wang, Y., Zhang, Z.: On-line Signature Verification Using Segment-to-segment Graph Matching. In: International Conference on Document Analysis and Recognition (ICDAR 2011), pp. 805–808 (2011)Google Scholar
  17. 17.
    Guru, D.S., Manjunatha, K.S., Manjunath, S.: User dependent features in online signature verification. In: Swamy, P.P., Guru, D.S. (eds.) ICMCCA 2012. LNEE, vol. 213, pp. 229–240. Springer, Heidelberg (2013)Google Scholar
  18. 18.
    Wang, J., Wu, L., Kong, J., Li, Y., Zhang, B.: Maximum weight and minimum redundancy: A novel framework for feature subset selection. Pattern Recognition 46, 1616–1627 (2013)CrossRefMATHGoogle Scholar
  19. 19.
    Kohavi, R., John, C.H.: Wrapper for feature subset selection. Artificial Intelligence 97, 273–324 (1997)CrossRefMATHGoogle Scholar
  20. 20.
    Kira, K., Rendell, L.: A practical approach to feature selection. In: Proceedings of 9th International Workshop on Machine Learning, pp. 249–256 (1992)Google Scholar
  21. 21.
    Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)MATHGoogle Scholar
  22. 22.
    Cai, D., Zhang, C., He, X.: Unsupervised Feature Selection for Multi-cluster Data. In: 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), pp. 333–342 (2010)Google Scholar
  23. 23.
    Guru, D.S., Prakash, H.N., Manjunath, S.: Online Signature Verification: An approach based on Cluster Representation of Global Features. In: Seventh International Conference on Advances in Pattern Recognition, pp. 209–212 (2009)Google Scholar
  24. 24.
    Nanni, L.: Experimental Comparison of One-class Classifier for On-line Signature Verification. Neurocomputing 69, 869–873 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • D. S. Guru
    • 1
  • K. S. Manjunatha
    • 1
  • S. Manjunath
    • 2
  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia
  2. 2.Department of Studies in Computer ScienceJSS College of Arts, Commerce and ScienceMysoreIndia

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