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Combined Off-Line Signature Verification Using Neural Networks

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Part of the Communications in Computer and Information Science book series (CCIS,volume 101)

Abstract

In this paper, combined off-line signature verification using Neural Network (CSVNN) is presented. The global and grid features are combined to generate new set of features for the verification of signature. The Neural Network (NN) is used as a classifier for the authentication of a signature. The performance analysis is verified on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm.

Keywords

  • Signature
  • Neural Network
  • FAR
  • FRR
  • Grid features and Global feature

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  • DOI: 10.1007/978-3-642-15766-0_99
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References

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Shashi Kumar, D.R., Ravi Kumar, R., Raja, K.B., Chhotaray, R.K., Pattanaik, S. (2010). Combined Off-Line Signature Verification Using Neural Networks. In: Das, V.V., Vijaykumar, R. (eds) Information and Communication Technologies. ICT 2010. Communications in Computer and Information Science, vol 101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15766-0_99

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  • DOI: https://doi.org/10.1007/978-3-642-15766-0_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15765-3

  • Online ISBN: 978-3-642-15766-0

  • eBook Packages: Computer ScienceComputer Science (R0)