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Identity verification based on haptic handwritten signatures: genetic programming with unbalanced data

Par Conseil national de recherches du Canada

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DOITrouver le DOI : https://doi.org/10.1109/CISDA.2012.6291531
AuteurRechercher : ; Rechercher : 1; Rechercher :
Affiliation
  1. Conseil national de recherches du Canada. Technologies de l'information et des communications
FormatTexte, Article
Conférence2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications (CISDA), July 11-13, 2012, Ottawa, Ontario, Canada
SujetHaptics; Biometrics; Genetic Programming; user verification; classification
Résumé
In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favorably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.
Date de publication
Dans
  • (CISDA 2012): Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defence Applications (13 juillet 2012) : 1–7.
Langueanglais
Publications évaluées par des pairsOui
Numéro NPARC20794318
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Identificateur de l’enregistrement796e046d-8a93-4645-902e-903f132c5dbd
Enregistrement créé2012-10-12
Enregistrement modifié2020-04-21
Date de modification :