Arnaud Dapogny

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Facial expression can be seen as the dynamic variation of one's appearance over time. Successful recognition thus involves finding representations of high-dimensional spatio-temporal patterns that can be generalized to unseen facial morphologies and variations of the expression dynamics. In this paper, we propose to learn Random Forests from heterogeneous(More)
— Automatic facial expression classification is a challenging problem for developing intelligent human-computer interaction systems. In order to take into account the expression dynamics, existing works usually make the assumption that a specific facial expression is displayed with a pre-segmented evolution, i.e. starting from neutral and finishing on an(More)
Automatic facial expression classification (FER) from videos is a critical problem for the development of intelligent human-computer interaction systems. Still, it is a challenging problem that involves capturing high-dimensional spatio-temporal patterns describing the variation of one's appearance over time. Such representation undergoes great variability(More)
Fully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the variability may not be fully covered by(More)
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