Local 3D Shape Analysis for Facial Expression ...
Type de document :
Communication dans un congrès avec actes
Titre :
Local 3D Shape Analysis for 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]
Department of Statistics [Tallahassee, FL]
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]
Department of Statistics [Tallahassee, FL]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Titre de la manifestation scientifique :
20th International Conference on Pattern Recognition (ICPR 2010)
Ville :
Istanbul
Pays :
Turquie
Date de début de la manifestation scientifique :
2010-08
Titre de l’ouvrage :
20th International Conference on Pattern Recognition (ICPR 2010)
Date de publication :
2010-08
Mot(s)-clé(s) en anglais :
3D facial expression recognition
binary classification
shape analysis
binary classification
shape analysis
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces ...
Lire la suite >We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces are extracted and represented by sets of closed curves. A Riemannian framework is used to derive the shape analysis of the extracted patches. The applied framework permits to calculate a similarity (or dissimilarity) distances between patches, and to compute the optimal deformation between them. Once calculated, these measures are employed as inputs to a commonly used classification techniques such as AdaBoost and Support Vector Machines (SVM). A quantitative evaluation of our novel approach is conducted on a subset of the publicly available BU-3DFE database.Lire moins >
Lire la suite >We investigate the problem of facial expression recognition using 3D face data. Our approach is based on local shape analysis of several relevant regions of a given face scan. These regions or patches from facial surfaces are extracted and represented by sets of closed curves. A Riemannian framework is used to derive the shape analysis of the extracted patches. The applied framework permits to calculate a similarity (or dissimilarity) distances between patches, and to compute the optimal deformation between them. Once calculated, these measures are employed as inputs to a commonly used classification techniques such as AdaBoost and Support Vector Machines (SVM). A quantitative evaluation of our novel approach is conducted on a subset of the publicly available BU-3DFE database.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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- ICPR10.pdf
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