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Proceedings Paper

Classification of handwritten signatures based on name legibility
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Paper Abstract

An automatic classification scheme of on-line handwritten signatures is presented. A Multilayer Perceptron (MLP) with a hidden layer is used as classifier, and two different signature classes are considered, namely: legible and non-legible name. Signatures are represented considering different feature subsets obtained from global information. Mahalanobis distance is used to rank the parameters and feature selection is then applied based on the top ranked features. Experimental results are given on the MCYT signature database comprising 330 signers. It is shown experimentally that automatic on-line signature classification based on the name legibility is feasible.

Paper Details

Date Published: 12 April 2007
PDF: 9 pages
Proc. SPIE 6539, Biometric Technology for Human Identification IV, 653907 (12 April 2007); doi: 10.1117/12.719236
Show Author Affiliations
Javier Galbally, Univ. Autónoma de Madrid (Spain)
Julian Fierrez, Univ. Autónoma de Madrid (Spain)
Javier Ortega-Garcia, Univ. Autónoma de Madrid (Spain)

Published in SPIE Proceedings Vol. 6539:
Biometric Technology for Human Identification IV
Salil Prabhakar; Arun A. Ross, Editor(s)

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