• DocumentCode
    183452
  • Title

    Online Signature Verification Based on Kolmogorov-Smirnov Distribution Distance

  • Author

    Griechisch, Erika ; Malik, Muhammad Imran ; Liwicki, Marcus

  • Author_Institution
    MTA-SZTE Res. Group on Artificial Intell., Univ. of Szeged, Szeged, Hungary
  • fYear
    2014
  • fDate
    1-4 Sept. 2014
  • Firstpage
    738
  • Lastpage
    742
  • Abstract
    Online signature verification methods examine the dynamics of the handwriting process to decide whether a signature is probably genuine or forged. Most of the previously proposed methods for online signature verification apply Neural Networks, Dynamic Time Warping, or Hidden Markov Model for classification and they consider several aspects, like planar coordinates, pressure, velocity, and acceleration with respect to time. Here we apply a non-parametric statistical test for a comparison of features and the verification of signatures.
  • Keywords
    digital signatures; feature extraction; handwriting recognition; hidden Markov models; neural nets; statistical distributions; time warp simulation; Kolmogorov-Smirnov distribution distance; dynamic time warping; feature extraction; handwriting process dynamics; hidden Markov model; neural networks; online signature verification; Distribution functions; Error analysis; Handwriting recognition; Hidden Markov models; Neural networks; Time factors; distance measures; handwritten signatures; online signature verification; statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on
  • Conference_Location
    Heraklion
  • ISSN
    2167-6445
  • Print_ISBN
    978-1-4799-4335-7
  • Type

    conf

  • DOI
    10.1109/ICFHR.2014.129
  • Filename
    6981108