3D Dynamic Expression Recognition Based ...
Type de document :
Communication dans un congrès avec actes
Titre :
3D Dynamic Expression Recognition Based on a Novel Deformation Vector Field and Random Forest
Auteur(s) :
Drira, Hassen [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Mohamed, Daoudi [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Anuj, Srivastava [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
Mohamed, Daoudi [Auteur]
FOX MIIRE [LIFL]
Institut TELECOM/TELECOM Lille1
Anuj, Srivastava [Auteur]
Department of Statistics [Tallahassee, FL]
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Titre de la manifestation scientifique :
21st International Conference on Pattern Recognition
Ville :
Tsukuba
Pays :
Japon
Date de début de la manifestation scientifique :
2012-11-11
Titre de l’ouvrage :
21st International Conference on Pattern Recognition
Date de publication :
2012-11-11
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
This paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial ...
Lire la suite >This paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial shape analysis and captures densely dynamic information from the entire face. The resulting temporal vector field is used to build the feature vector for expression recognition from 3D dynamic faces. By applying LDA-based feature space transformation for dimensionality reduction which is followed by a Multiclass Random Forest learning algorithm, the proposed approach achieved 93% average recognition rate on BU-4DFE database and outperforms state-of-art approaches.Lire moins >
Lire la suite >This paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial shape analysis and captures densely dynamic information from the entire face. The resulting temporal vector field is used to build the feature vector for expression recognition from 3D dynamic faces. By applying LDA-based feature space transformation for dimensionality reduction which is followed by a Multiclass Random Forest learning algorithm, the proposed approach achieved 93% average recognition rate on BU-4DFE database and outperforms state-of-art approaches.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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