Signature Verification with Dynamic RBF Networks and Time Series Motifs

Abstract : This article presents a novel classification algorithm for (multivariate) time series. In a first step, so-called time series motifs, which represent characteristic subsequences of the time series, are extracted using extreme points. In a second step, the extracted motifs are used to train a dynamic radial basis function network (DRBF). Compared to a standard radial basis function network, this DRBF has the advantage, that not only similar motifs of the same class are detected but also sequences of these motifs. For performance evaluation, the proposed classification algorithm is applied to online signature verification. Our experiments show, that the presented DRBF based on time series motifs is capable of a very reliable authentication with an equal error rate of about 1.5%.
Type de document :
Communication dans un congrès
Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00104508
Contributeur : Anne Jaigu <>
Soumis le : vendredi 6 octobre 2006 - 16:38:56
Dernière modification le : vendredi 6 octobre 2006 - 16:47:38
Document(s) archivé(s) le : mardi 6 avril 2010 - 18:52:40

Identifiants

  • HAL Id : inria-00104508, version 1

Collections

Citation

Christian Gruber, Michael Coduro, Bernhard Sick. Signature Verification with Dynamic RBF Networks and Time Series Motifs. Guy Lorette. Tenth International Workshop on Frontiers in Handwriting Recognition, Oct 2006, La Baule (France), Suvisoft, 2006. 〈inria-00104508〉

Partager

Métriques

Consultations de la notice

309

Téléchargements de fichiers

445