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Offline geometric parameters for automatic signature verification using fixed-point arithmetic.

Miguel A Ferrer1, Jesús B Alonso, Carlos M Travieso

  • 1Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Campus de Tafira, E35017 Las Palmas de Gran Canaria, Spain. mferrer@dsc.ulpgc.es

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 10, 2005
PubMed
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This study introduces new geometric features for automatic signature verification, effectively distinguishing genuine signatures from forgeries using various classification methods.

Area of Science:

  • Computer Science
  • Biometrics
  • Pattern Recognition

Background:

  • Automatic signature verification is crucial for security.
  • Existing methods face challenges with sophisticated forgeries.

Purpose of the Study:

  • To develop novel geometric signature features for improved offline signature verification.
  • To evaluate the effectiveness of these features against random and simple forgeries.

Main Methods:

  • Feature extraction based on signature envelope and stroke distribution (polar/Cartesian coordinates).
  • Implementation using 16-bit fixed-point arithmetic.
  • Classification using Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Euclidean Distance Classifier (EDC).

Main Results:

Related Experiment Videos

  • The proposed geometric features demonstrate high accuracy in discriminating forgeries.
  • Promising results achieved across different classifiers, indicating robustness.
  • Effective differentiation between genuine signatures and both random and simple forgeries.

Conclusions:

  • The developed geometric signature features offer a promising approach for offline signature verification.
  • The feature set shows effectiveness with various machine learning classifiers.
  • This method contributes to advancing the field of biometric security through signature analysis.