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Online Handwritten Signature Verification Using Hidden Markov Models

  • Juan J. Igarza
  • Iñaki Goirizelaia
  • Koldo Espinosa
  • Inmaculada Hernáez
  • Raúl Méndez
  • Jon Sánchez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability.

Keywords

Hide Markov Model Equal Error Rate Verification Process False Acceptance Rate Signature Verification 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Juan J. Igarza
    • 1
  • Iñaki Goirizelaia
    • 1
  • Koldo Espinosa
    • 1
  • Inmaculada Hernáez
    • 1
  • Raúl Méndez
    • 1
  • Jon Sánchez
    • 1
  1. 1.Department of Electronics and TelecommunicationsUniversity of the Basque CountryBilbaoSpain

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