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Proceedings Paper

Verification of the authenticity of handwritten signature using structure neural-network-type OCON
Author(s): M. L. Molina; N. A. Arias; Oscar Gualdron
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Paper Abstract

A method in order to carry out the verification of handwritten signatures is described. The method keeps in mind global features and local features that encode the shape and the dynamics of the signatures. Signatures are recorded with a digital tablet that can read the position and pressure of the pen. Input patterns are considered time and space dependent. Before extracting the information of the static features such as total length or height/width ratio, and the dynamic features such as speed or acceleration, the signature is normalized for position, size and orientation using its Fourier Descriptors. The comparison stage is carried out for algorithms of neurals networks. For each one of the sets of features a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage networks multilayer perceptron with few neurons are used. The classifier combines the decision results of the neural networks and the Euclidean distance obtained using the two feature sets. The results of the first-stage classifier feed a second-stage radial basis function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested, 160 neurals networks has been implemented.

Paper Details

Date Published: 21 October 2004
PDF: 6 pages
Proc. SPIE 5622, 5th Iberoamerican Meeting on Optics and 8th Latin American Meeting on Optics, Lasers, and Their Applications, (21 October 2004); doi: 10.1117/12.590754
Show Author Affiliations
M. L. Molina, Univ. de Pamplona (Colombia)
N. A. Arias, Univ. de Pamplona (Colombia)
Oscar Gualdron, Univ. Industrial de Santander (Colombia)


Published in SPIE Proceedings Vol. 5622:
5th Iberoamerican Meeting on Optics and 8th Latin American Meeting on Optics, Lasers, and Their Applications

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