Emmanuel Dellandréa

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Textured 3D face models capture precise facial surfaces along with the associated textures, making it possible for an accurate description of facial activities. In this paper, we present a unified probabilistic framework based on a novel Bayesian Belief Network (BBN) for 3D facial expression and Action Unit (AU) recognition. The proposed BBN performs(More)
We investigate the role that geometric, tex-tual and visual features play in the task of predicting a preposition that links two visual entities depicted in an image. The task is an important part of the subsequent process of generating image descriptions. We explore the prediction of prepositions for a pair of entities, both in the case when the labels of(More)
On one hand, the fact that Galvanic Skin Response (GSR) is highly correlated with the user affective arousal provides the possibility to apply GSR in emotion detection. On the other hand, temporal correlation of real-time GSR and self-assessment of arousal has not been well studied. This paper confronts two modalities representing the induced emotion when(More)
—To contribute to the need for emotional databases and affective tagging, the LIRIS-ACCEDE is proposed in this paper. LIRIS-ACCEDE is an Annotated Creative Commons Emotional DatabasE composed of 9800 video clips extracted from 160 movies shared under Creative Commons licenses. It allows to make this database publicly available 1 without copyright issues.(More)
Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark(More)