Angular Contour Parameterization for Signature Identification

  • Authors:

  • Juan Carlos Briceño;Carlos M. Travieso;Miguel A. Ferrer;Jesús B. Alonso;Francisco Vargas

  • Affiliations:

  • Computer Science Department, University of Costa Rica Sede "Rodrigo Facio Brenes", San José Post-Code 2060;Department of Signals and Communications, Technological Centre for Innovation on Communication (CeTIC), University of Las Palmas de Gran Canaria, Las Palmas de G.C., Spain 35017;Department of Signals and Communications, Technological Centre for Innovation on Communication (CeTIC), University of Las Palmas de Gran Canaria, Las Palmas de G.C., Spain 35017;Department of Signals and Communications, Technological Centre for Innovation on Communication (CeTIC), University of Las Palmas de Gran Canaria, Las Palmas de G.C., Spain 35017;Departamento de Ingeniería Electrónica, Universidad de Antioquia, Colombia

  • Venue:
  • Computer Aided Systems Theory - EUROCAST 2009
  • Year:
  • 2009

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Abstract

This present work presents a parameterization system based on angles from signature edge (2D-shape) for off-line signature identification. We have used three different classifiers, the Nearest Neighbor classifier (K-NN), Neural Networks (NN) and Hidden Markov Models (HMM). Our off-line database has 800 writers with 24 samples per each writer; in total, 19200 images have been used in our experiments. We have got a success rate of 84.64%, applying as classifier Hidden Markov Model, and only used the information from this edge detection method.