Local 3D Shape Analysis for Facial Expression ...
Document type :
Communication dans un congrès avec actes
Title :
Local 3D Shape Analysis for Facial Expression Recognition
Author(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]
Conference title :
20th International Conference on Pattern Recognition (ICPR 2010)
City :
Istanbul
Country :
Turquie
Start date of the conference :
2010-08
Book title :
20th International Conference on Pattern Recognition (ICPR 2010)
Publication date :
2010-08
English keyword(s) :
3D facial expression recognition
binary classification
shape analysis
binary classification
shape analysis
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
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