Abstract
Automatic facial expression analysis systems try to build a mapping between the continuous emotion space and a set of discrete expression categories (e.g. happiness, sadness). In this paper, we present a method to recognize emotions in terms of latent dimensions (e.g. arousal, valence, power). The method we applied uses Gabor energy texture descriptors to model the facial appearance deformations, and a multiclass SVM as base learner of emotions. To deal with more naturalistic behavior, the SEMAINE database of naturalistic dialogues was used.
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Dahmane, M., Meunier, J. (2011). Continuous Emotion Recognition Using Gabor Energy Filters. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_46
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DOI: https://doi.org/10.1007/978-3-642-24571-8_46
Publisher Name: Springer, Berlin, Heidelberg
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