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A Hidden Markov Model approach to online handwritten signature verification

  • R. Kashi
  • J. Hu
  • W.L. Nelson
  • W. Turin

Abstract.

A method for the automatic verification of online handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that adding a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), to the global features of a signature, significantly improves the performance of verification. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.

Key words:Signature verification – Fourier descriptors – Hidden Markov Models – Viterbi decoding 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • R. Kashi
    • 1
  • J. Hu
    • 1
  • W.L. Nelson
    • 1
  • W. Turin
    • 2
  1. 1. Bell Labs, Lucent Technologies, Murray Hill, NJ, USA US
  2. 2. AT&T Research Labs, Florham Park, NJ, USA US

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