MING, Zuheng; BUGEAU, Aurélie; ROUAS, Jean-Luc; SHOCHI, Takaaki

doi:10.1109/FG.2015.7284870

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MING, Zuheng
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

BUGEAU, Aurélie
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

ROUAS, Jean-Luc
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

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Communication dans un congrès avec actes

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11th IEEE International Conference on Automatic Face and Gesture Recognition (FG), 11th IEEE International Conference on Automatic Face and Gesture Recognition, 2015-05-04, Ljubljana. p. 1-6

Résumé en anglais

— Automatic facial expression recognition has emerged over two decades. The recognition of the posed facial expressions and the detection of Action Units (AUs) of facial expression have already made great progress. More recently, the automatic estimation of the variation of facial expression, either in terms of the intensities of AUs or in terms of the values of dimensional emotions, has emerged in the field of the facial expression analysis. However, discriminating different intensities of AUs is a far more challenging task than AUs detection due to several intractable problems. Aiming to continuing standardized evaluation procedures and surpass the limits of the current research, the second Facial Expression Recognition and Analysis challenge (FERA2015) is presented. In this context, we propose a method using the fusion of the different appearance and geometry features based on a multi-kernel Support Vector Machine (SVM) for the automatic estimation of the intensities of the AUs. The result of our approach benefiting from taking advantages of the different features adapting to a multi-kernel SVM is shown to outperform the conventional methods based on the mono-type feature with single kernel SVM.< Réduire

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Multi-kernel SVM

Fusion features

Facial Action Unit Intensity

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Facial Action Units Intensity Estimation by the Fusion of Features with Multi-kernel Support Vector Machine

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