Efficient Off-Line Verification and Identification of Signatures by Multiclass Support Vector Machines

  • Emre Özgündüz
  • Tülin Şentürk
  • M. Elif Karslıgil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3691)


In this paper we present a novel and efficient approach for off-line signature verification and identification using Support Vector Machine. The global, directional and grid features of the signatures were used. In verification, one-against-all strategy is used. The true acceptance rate is 98% and true rejection rate is 81%. As the identification of signatures represent a multi-class problem, Support Vector Machine’s one-against-all and one-against-one strategies were applied and their performance were compared. Our experiments indicate that one-against-one with 97% true recognition rate performs better than one-against-all by 3%.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sansone, C., Vento, M.: Signature Verification: Increasing Performance by a Multi-Stage System. Pattern Analysis & Applications 3, 169–181 (2000)CrossRefGoogle Scholar
  2. 2.
    Justino, E.J.R., Bortolozzi, F., Sabourin, R.: Off-line Signature Verification Using HMM for Random, Simple and Skilled Forgeries. In: ICDAR 2001, International Conference on Document Analysis and Recognition, vol. 1, pp. 105–110 (2001)Google Scholar
  3. 3.
    Zhang, B., Fu, M., Yan, H.: Handwritten Signature Verification based on Neural ‘Gas’ Based Vector Quantization. In: IEEE International Joint Conference on Neural Networks, pp. 1862–1864 (May 1998)Google Scholar
  4. 4.
    Vélez, J.F., Sánchez, Á., Moreno, A.B.: Robust Off-Line Signature Verification Using Compression Networks And Positional Cuttings. In: Proc. 2003 IEEE Workshop on Neural Networks for Signal Processing, vol. 1, pp. 627–636 (2003)Google Scholar
  5. 5.
    Arif, M., Vincent, N.: Comparison of Three Data Fusion Methods For An Off-Line Signature Verification Problem. Laboratoire d’Informatique, Université de François Rabelais (2003)Google Scholar
  6. 6.
    Chalechale, A., Mertins, A.: Line Segment Distribution of Sketches for Persian Signature Recognition. In: IEEE Proc. TENCON, vol. 1, pp. 11–15 (October 2003)Google Scholar
  7. 7.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  8. 8.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998)MATHGoogle Scholar
  9. 9.
    Müller, K., Mika, S., et al.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks 12, 181–202 (2001)CrossRefGoogle Scholar
  10. 10.
    Baltzakis, H., Papamarkos, N.: A new signature verification technique based on a two-stage neural network classifier. In: Pergamon, 95–103 (2001)Google Scholar
  11. 11.
    Hilditch, C.J.: Linear skeletons from Square Cupboards. Machine Intelligence 4, 404–420 (1969)Google Scholar
  12. 12.
    Gómez-moreno, H., Gil-jiménez, P., Lafuente-arroyo, S., Vicen-bueno, R., Sánchez-montero, R.: Color images segmentation using the Support Vector Machines (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Emre Özgündüz
    • 1
  • Tülin Şentürk
    • 1
  • M. Elif Karslıgil
    • 1
  1. 1.Computer Engineering DepartmentYıldız Technical UniversityYıldız, IstanbulTurkey

Personalised recommendations