Fully Automatic 3D Facial Expression ...
Type de document :
Communication dans un congrès avec actes
Titre :
Fully Automatic 3D Facial Expression Recognition using Differential Mean Curvature Maps and Histograms of Oriented Gradients
Auteur(s) :
Lemaire, Pierre [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Chen, Liming [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Ardabilian, Mohsen [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Extraction de Caractéristiques et Identification [imagine]
Chen, Liming [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Ardabilian, Mohsen [Auteur]
Extraction de Caractéristiques et Identification [imagine]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Titre de la manifestation scientifique :
Workshop 3D Face Biometrics, IEEE Automatic Facial and Gesture Recognition
Ville :
Shanghai
Pays :
Chine
Date de début de la manifestation scientifique :
2013-04-22
Date de publication :
2013-04-22
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
In this paper, we propose an holistic, fully auto- matic approach to 3D Facial Expression Recognition (FER). A novel facial representation, namely Differential Mean Curva- ture Maps (DMCMs), is proposed to capture both ...
Lire la suite >In this paper, we propose an holistic, fully auto- matic approach to 3D Facial Expression Recognition (FER). A novel facial representation, namely Differential Mean Curva- ture Maps (DMCMs), is proposed to capture both global and local facial surface deformations which typically occur during facial expressions. These DMCMs are directly extracted from 3D depth images, by calculating the mean curvatures thanks to an integral computation. To account for facial morphology variations, they are further normalized through an aspect ratio deformation. Finally, Histograms of Oriented Gradients (HOG) are applied to regions of these normalized DMCMs and allow for the generation of facial features that can be fed to the widely used Multiclass-SVM classification algorithm. Using the protocol proposed by Gong et al. on the BU-3DFE dataset, the proposed approach displays competitive performance while staying entirely automatic.Lire moins >
Lire la suite >In this paper, we propose an holistic, fully auto- matic approach to 3D Facial Expression Recognition (FER). A novel facial representation, namely Differential Mean Curva- ture Maps (DMCMs), is proposed to capture both global and local facial surface deformations which typically occur during facial expressions. These DMCMs are directly extracted from 3D depth images, by calculating the mean curvatures thanks to an integral computation. To account for facial morphology variations, they are further normalized through an aspect ratio deformation. Finally, Histograms of Oriented Gradients (HOG) are applied to regions of these normalized DMCMs and allow for the generation of facial features that can be fed to the widely used Multiclass-SVM classification algorithm. Using the protocol proposed by Gong et al. on the BU-3DFE dataset, the proposed approach displays competitive performance while staying entirely automatic.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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