Efficient off-line verification and identification of signatures by multiclass support vector machines

ÖZGÜNDÜZ E., Şentürk T., Karsligil M. E.

11th International Conference on Computer Analysis of Images and Patterns, CAIP 2005, Versailles, France, 5 - 08 September 2005, pp.799-805 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1007/11556121_98
  • City: Versailles
  • Country: France
  • Page Numbers: pp.799-805
  • Yıldız Technical University Affiliated: Yes


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%. © Springer-Verlag Berlin Heidelberg 2005.