HapticV1, Main, Exploration, bibRecordById, Pascal:13-0216963

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Identifieur interne : 001F28 ( Main/Exploration ); précédent : 001F27; suivant : 001F29

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Auteurs : Fawaz A. Alsulaiman [Canada] ; Nizar Sakr [Canada] ; Julio J. Valdes [Canada] ; Abdulmotaleb El Saddik [Canada]

Source :

ACM transactions on multimedia computing communications and applications [ 1551-6857 ] ; 2013.

RBID : Pascal:13-0216963

Descripteurs français

Pascal (Inist)
- Signature électronique, Caractère manuscrit, Plus proche voisin, Apprentissage probabilités, Pertinence, Type donnée, Biométrie, Sensibilité tactile, Raisonnement basé sur cas, Apprentissage supervisé, Analyse n dimensionnelle, Cryptage basé identité, Algorithme génétique, Classification à vaste marge, Estimation Bayes, Extraction forme, Modélisation, Fonction analytique, ., Forêt aléatoire, Contrôle accès.
Wicri :
- topic : Signature électronique, Biométrie.

English descriptors

KwdEn :
- Access control, Analytical function, Bayes estimation, Biometrics, Case based reasoning, Data type, Digital signature, Genetic algorithm, Identity based encryption, Manuscript character, Modeling, Multidimensional analysis, Nearest neighbour, Pattern extraction, Probability learning, Random decision forests, Relevance, Supervised learning, Tactile sensitivity, Vector support machine.

Abstract

In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).

Affiliations:

Canada

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: 000191
to stream PascalFrancis, to step Curation: 001108
to stream PascalFrancis, to step Checkpoint: 000139
to stream Main, to step Merge: 001F50
to stream Main, to step Curation: 001F28

Le document en format XML

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<term>Data type</term>
<term>Digital signature</term>
<term>Genetic algorithm</term>
<term>Identity based encryption</term>
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<term>Pertinence</term>
<term>Type donnée</term>
<term>Biométrie</term>
<term>Sensibilité tactile</term>
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<front><div type="abstract" xml:lang="en">In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naïve Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).</div>
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Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Identity Verification Based on Handwritten Signatures with Haptic Information Using Genetic Programming

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri