Fully Automatic 3D Facial Expression Recognition using Differential Mean Curvature Maps and Histograms of Oriented Gradients

Pierre Lemaire 1 Liming Chen 1 Mohsen Ardabilian 1 Mohamed Daoudi 2, 3
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 LIFL - FOX MIIRE
LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : 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.
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Communication dans un congrès
Workshop 3D Face Biometrics, IEEE Automatic Facial and Gesture Recognition, Apr 2013, Shanghai, China. pp.1, 2013
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Contributeur : Mohamed Daoudi <>
Soumis le : dimanche 19 mai 2013 - 12:39:05
Dernière modification le : vendredi 21 octobre 2016 - 09:41:09
Document(s) archivé(s) le : mardi 20 août 2013 - 02:55:09

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  • HAL Id : hal-00823903, version 1

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Pierre Lemaire, Liming Chen, Mohsen Ardabilian, Mohamed Daoudi. Fully Automatic 3D Facial Expression Recognition using Differential Mean Curvature Maps and Histograms of Oriented Gradients. Workshop 3D Face Biometrics, IEEE Automatic Facial and Gesture Recognition, Apr 2013, Shanghai, China. pp.1, 2013. <hal-00823903>

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