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)

Abstract

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%.

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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

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