Bimodal 2D-3D face recognition using a two-stage fusion strategy

Aissaoui, Amel; Martinet, Jean

Type de document :

Communication dans un congrès avec actes

DOI :

10.1109/IPTA.2015.7367146

Titre :

Bimodal 2D-3D face recognition using a two-stage fusion strategy

Auteur(s) :

Aissaoui, Amel [Auteur]
Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger] [USTHB]
Martinet, Jean [Auteur]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la manifestation scientifique :

International Conference on Image Processing Theory, Tools and Applications

Ville :

Orléans

Pays :

France

Date de début de la manifestation scientifique :

2015-11-10

Titre de l’ouvrage :

International Conference on Image Processing Theory, Tools and Applications

Titre de la revue :

International Conference on Image Processing Theory, Tools and Applications

Date de publication :

2015-11

Mot(s)-clé(s) en anglais :

Face recognition
LBP
DLBP
Bimodal fusion

Discipline(s) HAL :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

Résumé en anglais : [en]

This paper presents a novel approach for bimodal face recognition. In our approach, faces are represented by both texture and depth images. The well known Local Binary Pattern (LBP) is used to describe the texture images. ...
Lire la suite >This paper presents a novel approach for bimodal face recognition. In our approach, faces are represented by both texture and depth images. The well known Local Binary Pattern (LBP) is used to describe the texture images. The depth faces representation is based on the Depth Local Binary Pattern, which is an extension of the the LBP descriptor allowing more discriminative power of smooth depth images. In order to perform the bimodal face recognition, a two-stage fusion scheme is proposed. It allows to take advantage of the complementarity of range and texture modalities at both descriptor (early fusion) and decision (late fusion) levels. We have conducted extensive experiments on several datasets in order to evaluate our approach. The obtained results show that our combination of texture and depth descriptors yields higher results than when taken separately or using an early/late fusion scheme.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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