Visualization of Dynamic Characteristics in Two-Dimensional Time Series Patterns: An Application to Online Signature Verification

  • Suyoung Chi
  • Jaeyeon Lee
  • Jung Soh
  • Dohyung Kim
  • Weongeun Oh
  • Changhun Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2908)


An analysis model for the dynamics information of two-dimensional time-series patterns is described. In the proposed model, two novel transforms that visualize the dynamic characteristics are proposed. The first transform, referred to as speed equalization, reproduces a time-series pattern assuming a constant linear velocity to effectively model the temporal characteristics of the signing process. The second transform, referred to as velocity transform, maps the signal onto a horizontal vs. vertical velocity plane where the variation of the velocities over time is represented as a visible shape. With the transforms, the dynamic characteristics in the original signing process are reflected in the shape of the transformed patterns. An analysis in the context of these shapes then naturally results in an effective analysis of the dynamic characteristics. The proposed transform technique is applied to an online signature verification problem for evaluation. Experimenting on a large signature database, the performance evaluated in EER(Equal Error Rate) was improved to 1.17% compared to 1.93% of the traditional signature verification algorithm in which no transformed patterns are utilized. In the case of skilled forgery experiments, the improvement was more outstanding; it was demonstrated that the parameter set extracted from the transformed patterns was more discriminative in rejecting forgeries


Feature Vector Dynamic Characteristic Equal Error Rate Original Pattern Signature Verification 
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  1. 1.
    Kashi, R., Hu, J., Nelson, W.L., Turin, W.: A Hidden Markov Model Approach to online handwritten signature verification. International Journal on Document Analysis & Recognition 1(2), 102–109 (1998)CrossRefGoogle Scholar
  2. 2.
    Wijesoma, W.S., Mingming, M., Sung, E.: Selecting Optimal Personalized Features for Online Signature Verification Using GA. In: Proc. SMC 2000, vol. 4, pp. 2740–2745 (2000)Google Scholar
  3. 3.
    Fairhust, M.C., Ng, S.: Management of access through biometric control: A case study based on automatic signature verification. Universal Access in the Information Society 1(1), 31–39 (2001)Google Scholar
  4. 4.
    Gonzalez, R.C., Wintz, P.: Digital Image Processing, pp. 78–87. Addison-Wesley Publishing Company, Inc., Reading (1977)zbMATHGoogle Scholar
  5. 5.
    Wessels, T., Omlin, C.W.: A Hybrid System for Signature Verification. In: Proc. IJCNN 2000, vol. 5, pp. 509–514 (2000)Google Scholar
  6. 6.
    Burden, R.L., Faires, J.D.: Numerical Analysis, 5th edn., pp. 57–168. International Thomson Publishing (1993)Google Scholar
  7. 7.
    Fairhust, M.C.: Signature Verification Revisited: promoting practical exploitation of biometric technology. Electronics & Communication Engineering Journal 9(6), 273–280 (1997)CrossRefGoogle Scholar
  8. 8.
    Plamondon, R., Lorette, G.: Automatic Signature Verification and Writer Identification – The State of the Art. Pattern Recognition 22(2), 107–131 (1989)CrossRefGoogle Scholar
  9. 9.
    Leclerc, F., Plamondon, R.: Automatic Signature Verification: The State of the Art – 1989-1993. International Journal of Pattern Recognition & Artificial Intelligence 8(3), 634–660 (1994)Google Scholar
  10. 10.
    Plamondon, R., Srihari, S.: On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey. IEEE Trans. Pattern Analysis & Matchine Intelligence 22(1), 63–84 (2000)CrossRefGoogle Scholar
  11. 11.
    Huang, K., Yan, H.: Off-Line Signature Verification based on Geometric Feature Extraction and Neural Network Classification. Pattern Recognition 30(1), 9–17 (1997)CrossRefGoogle Scholar
  12. 12.
    Martens, R., Classen, L.: Incorporating local consistency information into the online signature verification process. International Journal on Document Analysis & Recognition 1(2), 110–115 (1998)CrossRefGoogle Scholar
  13. 13.
    Ribeiro, J., Vasconcelos, G.: Off-Line Signature Verification Using an Auto-associator Cascade-Correlation Architecture. In: Proc. International Joint Conference on Neural Networks, vol. 4, pp. 2882–2886 (1999)Google Scholar
  14. 14.
    El-Yacoubi, A., Justino, E.J.R., Sabourin, R., Bortolizzi, F.: Off-Line Signature Verification Using HMMs and Cross-Validation. In: Proc. Signal Processing Society Workshop 2000, vol. 2, pp. 859–868 (2000)Google Scholar
  15. 15.
    Justino, E.J.R., Bortolozzi, F., Babourin, R.: Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries. In: Proc. 6th International Conf. on Document Analysis and Recognition, pp. 1031–1034 (2001)Google Scholar
  16. 16.
    Sabourin, R., Genest, G., Preteux, F.J.: Off-Line Signature Verification by Local Granulometric Size Distributions. IEEE Trans. Pattern Analysis & Machine Intelligence 19(9), 976–988 (1997)CrossRefGoogle Scholar
  17. 17.
    Kecman, V.: Learning and Soft Computing, pp. 255–312. The MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  18. 18.
    Zhang, K., Pratikakis, I., Cornelis, J., Nyssen, E.: Using Landmarks to Establish a Pointto-Point Correspondence between Signatures. Pattern Analysis & Applications 3(1), 69–74 (2000)CrossRefGoogle Scholar
  19. 19.
    Kiran, G.V., Kunte, R.S., Samuel, S.: On-Line Signature Verification System Using Probablistic Feature Modeling. In: Proc. 6th International Symposium on Signal Processing and its Applications, vol. 1, pp. 355–358 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Suyoung Chi
    • 1
  • Jaeyeon Lee
    • 1
  • Jung Soh
    • 1
  • Dohyung Kim
    • 1
  • Weongeun Oh
    • 1
  • Changhun Kim
    • 2
  1. 1.Computer & Software Technology LaboratoryETRIDaejeonKOREA
  2. 2.Korea UniversitySeoulKorea

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