A Set of Selected SIFT Features for 3D ...
Document type :
Communication dans un congrès avec actes
Title :
A Set of Selected SIFT Features for 3D Facial Expression Recognition
Author(s) :
Berretti, Stefano [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
del Bimbo, Alberto [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Pala, Pietro [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Mohamed, Daoudi [Auteur]
FOX MIIRE [LIFL]
Dipartimento di Sistemi e Informatica [DSI]
del Bimbo, Alberto [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Pala, Pietro [Auteur]
Dipartimento di Sistemi e Informatica [DSI]
Ben Amor, Boulbaba [Auteur]
FOX MIIRE [LIFL]
Mohamed, Daoudi [Auteur]
FOX MIIRE [LIFL]
Conference title :
20th International Conference on Pattern Recognition
City :
Istanbul
Country :
Turquie
Start date of the conference :
2010-08-23
Book title :
20th International Conference on Pattern Recognition (ICPR), 2010
Publication date :
2010-08-23
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [en]
In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that computes SIFT descriptors on a set of facial landmarks of depth ...
Show more >In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that computes SIFT descriptors on a set of facial landmarks of depth images, and then selects the subset of most relevant features. Using SVM classification of the selected features, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparative evaluation on a common experimental setup, shows that our solution is able to obtain state of the art results.Show less >
Show more >In this paper, the problem of person-independent facial expression recognition is addressed on 3D shapes. To this end, an original approach is proposed that computes SIFT descriptors on a set of facial landmarks of depth images, and then selects the subset of most relevant features. Using SVM classification of the selected features, an average recognition rate of 77.5% on the BU-3DFE database has been obtained. Comparative evaluation on a common experimental setup, shows that our solution is able to obtain state of the art results.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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