Shape analysis of local facial patches for ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Shape analysis of local facial patches for 3D facial expression recognition
Auteur(s) :
Maalej, Ahmed [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Srivastava, Anuj [Auteur]
Florida State University [Tallahassee] [FSU]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Srivastava, Anuj [Auteur]
Florida State University [Tallahassee] [FSU]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Titre de la revue :
Pattern Recognition
Pagination :
1581-1589
Éditeur :
Elsevier
Date de publication :
2011-02
ISSN :
0031-3203
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
In this paper we address the problem of 3D facial expression recognition. We propose a local geometric shape analysis of facial surfaces coupled with machine learning techniques for expression classification. A computation ...
Lire la suite >In this paper we address the problem of 3D facial expression recognition. We propose a local geometric shape analysis of facial surfaces coupled with machine learning techniques for expression classification. A computation of the length of the geodesic path between corresponding patches, using a Riemannian framework, in a shape space provides a quantitative information about their similarities. These measures are then used as inputs to several classification methods. The experimental results demonstrate the effectiveness of the proposed approach. Using Multi-boosting and Support Vector Machines (SVM) classifiers, we achieved 98.81% and 97.75% recognition average rates, respectively, for recognition of the six prototypical facial expressions on BU-3DFE database. A comparative study using the same experimental setting shows that the suggested approach outperforms previous work.Lire moins >
Lire la suite >In this paper we address the problem of 3D facial expression recognition. We propose a local geometric shape analysis of facial surfaces coupled with machine learning techniques for expression classification. A computation of the length of the geodesic path between corresponding patches, using a Riemannian framework, in a shape space provides a quantitative information about their similarities. These measures are then used as inputs to several classification methods. The experimental results demonstrate the effectiveness of the proposed approach. Using Multi-boosting and Support Vector Machines (SVM) classifiers, we achieved 98.81% and 97.75% recognition average rates, respectively, for recognition of the six prototypical facial expressions on BU-3DFE database. A comparative study using the same experimental setting shows that the suggested approach outperforms previous work.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Collections :
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