Online Signature Verification with New Time Series Kernels for Support Vector Machines

  • Christian Gruber
  • Thiemo Gruber
  • Bernhard Sick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


In this paper, two new methods for online signature verification are proposed. The methods adopt the idea of the longest common subsequences (LCSS) algorithm to a kernel function for Support Vector Machines (SVM). The two kernels LCSS-global and LCSS-local offer the possibility to classify time series of different lengths with SVM. The similarity of two time series is determined very accurately since outliers are ignored. Consequently, LCSS-global and LCSS-local are more robust than algorithms based on dynamic time alignment such as Dynamic Time Warping (DTW). The new methods are compared to other kernel-based methods (DTW-kernel, Fisher-kernel, Gauss-kernel). Our experiments show that SVM with LCSS-local and LCSS-global authenticate persons very reliably.


Support Vector Machine Kernel Function Gaussian Mixture Model Dynamic Time Warping Multivariate Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Thelusma, F., Muherjee, S.: Signature Verification, Internal Report, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts (06/03/2005),
  2. 2.
    Fuentes, M., Garcia-Salicetti, S., Dorizzi, B.: On-Line Signature Verification: Fusion of a Hidden Markov Model and a Neural Network via a Support Vector Machine. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR 2002), Ontario, Canada, pp. 253–258 (2002)Google Scholar
  3. 3.
    Wan, V.: Speaker Verification using Support Vector Machines, Ph.D. Thesis, University of Sheffield, GB (2003)Google Scholar
  4. 4.
    Rüping, S.: SVM Kernels for Time Series Analysis. In: Tagungsband der GI-Workshop-Woche LLWA 2001, Dortmund, pp. 43–50 (2001)Google Scholar
  5. 5.
    Chakrabartty, S., Deng, Y.: Dynamic Time Alignment in Support Vector Machines for Recognition Systems, Internal Report, The John Hopkins University, Baltimore (06/03/2005),
  6. 6.
    Shimodaira, H., Noma, K., Nakai, M., Sagayama, S.: Dynamic Time-Alignment Kernel in Support Vector Machine. In: Advances in Neural Information Processing (NIPS 2001) 14, 921–928 (2001)Google Scholar
  7. 7.
    Shimodaira, H., Noma, K., Nakai, M., Sagayama, S.: Support Vector Machine with Dynamic Time-Alignment Kernel for Speech Recognition. In: Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), Aalborg, vol. 3, pp. 1841–1844 (2001)Google Scholar
  8. 8.
    Bahlmann, C., Haasdonk, B., Burkhardt, H.: On-line Handwriting Recognition with Support Vector Machines – A Kernel Approach. In: 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR 2002), Ontario, pp. 49–54 (2002)Google Scholar
  9. 9.
    Jaakkola, T., Diekhans, M., Haussler, D.: Using the Fisher Kernel Method to Detect Remote Protein Homologies. In: 7th International Conference on Intelligent Systems for Molecular Biology, Menlo Park, CA, pp. 149–158 (1999)Google Scholar
  10. 10.
    Moreno, P.J., Ho, P.P., Vasconcelos, N.: A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications. HP Laboratories Cambridge, Tech. Report HPL-2004-4 (2004)Google Scholar
  11. 11.
    Das, G., Gunopulos, D., Mannila, H.: Finding Similar Time Series. In: 1st European Symposium on Principles of Data Mining and Knowledge Discovery, Trondheim, pp. 88–100 (1997)Google Scholar
  12. 12.
    Bollobás, B., Das, G., Gunopulos, D., Mannila, H.: Time-Series Similarity Problems and Well-Separated Geometric Sets. In: Proceedings of the 13th Annual ACM Symposium on Computational Geometry, Nice, pp. 454–456 (1997)Google Scholar
  13. 13.
    Agrawal, R., Lin, K.-I., Sawhney, H.S., Shim, K.: Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases. In: 21st International Conference on Very Large Databases, Zürich, pp. 490–501 (1995)Google Scholar
  14. 14.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)CrossRefGoogle Scholar
  15. 15.
    Hook, C., Kempf, J., Scharfenberg, G.: A Novel Digitizing Pen for the Analysis of Pen Pressure and Inclination in Handwriting Biometrics. In: Maltoni, D., Jain, A.K. (eds.) BioAW 2004. LNCS, vol. 3087, pp. 283–294. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Tsuda, K., Kawanabe, M., Rötsch, G., Sonnenburg, S., Müller, K.-R.: A new discriminative kernel from probabilistic models. Neural Computation 14(10), 2397–2414 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Christian Gruber
    • 1
  • Thiemo Gruber
    • 1
  • Bernhard Sick
    • 1
  1. 1.Institute of Computer ArchitecturesUniversity of Passau 

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