Affective Computing Focus on Emotion Expression Synthesis by vedran kordic

By vedran kordic

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Extra info for Affective Computing Focus on Emotion Expression Synthesis and Recognition

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We propose two different approaches. The first one is a non-parametric approach and relies on Dynamic Time Warping. The second one is a parametric approach and is based on Linear Discriminant Analysis. In order to learn the spatio-temporal structure of the facial actions associated with the universal expressions, we have used the following. , 2000). These sequences depict five frontal view universal expressions (surprise, sadness, joy, disgust and anger). Each expression is performed by 7 different subjects, starting from the neutral one.

Friesen. Facial Action Coding System. Consulting Psychology Press, Palo Alto, CA, USA, 1977. B. Fasel and J. Luettin. Automatic facial expression analysis: A survey. Pattern Recognition, 36(1):259-275, 2003. B. Y. Bouguet, C. Tomasi, and B. Girod. Model-based face tracking for viewindependent facial expression recognition. In IEEE International Conference on Automatic Face and Gesture Recognition, 2002. Y. Huang, T. S. Huang, and H. Niemann. A region-based method for model-free object tracking.

For this sequence, the maximum probability was correctly indicating the displayed expression. We noticed that some displayed expressions can, during a short initial phase (very few frames), be considered as a mixture of two expressions (the displayed one and another one). This is due to the fact that face postures and dynamics at some transition phases can be shared by more than one expression. This is not a problem since the framewise expression probabilities can be merged and averaged over a temporal patch including contiguous non-neutral frames.

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