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Designing systems able to interact with humans in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the Audio/Visual Emotion Challenge (AVEC'12) whose goal is to continuously predict four affective signals(More)
This paper presents our response to the first international challenge on facial emotion recognition and analysis. We propose to combine different types of features to automatically detect action units (AUs) in facial images. We use one multikernel support vector machine (SVM) for each AU we want to detect. The first kernel matrix is computed using local(More)
— In this paper we present a robust and accurate method to detect 17 facial landmarks in expressive face images. We introduce a new multi-resolution framework based on the recent multiple kernel algorithm. Low resolution patches carry the global information of the face and give a coarse but robust detection of the desired landmark. High resolution patches,(More)
— This study presents a combination of geometric and appearance features used to automatically detect Action Units in face images. We use one multi-kernel SVM for each Action Unit we want to detect. The first kernel matrix is computed using Local Gabor Binary Pattern (LGBP) histograms and a histogram intersection kernel. The second kernel matrix is computed(More)
This article presents an appearance based method to detect automatically facial actions. Our approach fo-cuses on reducing features sensitivity to identity of the subject. We compute from an expressive image a Local Gabor Binary Pattern (LGBP) histogram and synthesize a LGBP histogram approaching the one we would compute on a neutral face. Difference(More)
In this paper, we investigate the interest of action unit (AU) detection for automatic emotion recognition. We propose and compare two emotion detectors: the first works directly on a high-dimensional feature space and the second projects facial image in the low-dimensional space of AU intensities before recognizing emotion. In both approaches, facial(More)
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)
The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high(More)
— The problem of learning several related tasks has recently been addressed with success by the so-called multi-task formulation, that discovers underlying common structure between tasks. Metric Learning for Kernel Regression (MLKR) aims at finding the optimal linear subspace for reducing the squared error of a Nadaraya-Watson estimator. In this paper, we(More)