A Grassmann Framework for 4D Facial Shape Analysis
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
A Grassmann Framework for 4D Facial Shape Analysis
Auteur(s) :
Alashkar, Taleb [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Ben Amor, Boulbaba [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Daoudi, Mohamed [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Berretti, Stefano [Auteur]
Università degli Studi di Firenze = University of Florence = Université de Florence [UniFI]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Ben Amor, Boulbaba [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Daoudi, Mohamed [Auteur]
Modeling and Analysis of Static and Dynamic Shapes [3D-SAM]
Berretti, Stefano [Auteur]
Università degli Studi di Firenze = University of Florence = Université de Florence [UniFI]
Titre de la revue :
Pattern Recognition
Pagination :
21-30
Éditeur :
Elsevier
Date de publication :
2016-09
ISSN :
0031-3203
Mot(s)-clé(s) en anglais :
Grassmann manifold
Curvature-maps
Sparse coding
Dictionary learning
4D Face recognition
Curvature-maps
Sparse coding
Dictionary learning
4D Face recognition
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
In this paper, we investigate the contribution of dynamic evolution of 3D faces to identity recognition. To this end, we adopt a subspace representation of the flow of curvature-maps computed on 3D facial frames of a ...
Lire la suite >In this paper, we investigate the contribution of dynamic evolution of 3D faces to identity recognition. To this end, we adopt a subspace representation of the flow of curvature-maps computed on 3D facial frames of a sequence, after normalizing their pose. Such representation allows us to embody the shape as well as its temporal evolution within the same subspace representation. Dictionary learning and sparse coding over the space of fixed-dimensional subspaces, called Grassmann manifold, have been used to perform face recognition. We have conducted extensive experiments on the BU-4DFE dataset. The obtained results of the proposed approach provide promising results.Lire moins >
Lire la suite >In this paper, we investigate the contribution of dynamic evolution of 3D faces to identity recognition. To this end, we adopt a subspace representation of the flow of curvature-maps computed on 3D facial frames of a sequence, after normalizing their pose. Such representation allows us to embody the shape as well as its temporal evolution within the same subspace representation. Dictionary learning and sparse coding over the space of fixed-dimensional subspaces, called Grassmann manifold, have been used to perform face recognition. We have conducted extensive experiments on the BU-4DFE dataset. The obtained results of the proposed approach provide promising results.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :