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By vedran kordic

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

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Figure 13: Simultaneous tracking and recognition associated with a 600-frame video sequence depicting non- frontal head poses. 38 Affective Computing, Focus on Emotion Expression, Synthesis and Recognition Figure 14: Top: The probability of each expression as a function of time associated with a 1600-frame video sequence. Bottom: The tracked facial actions associated with the subject's speech which starts at frame 900 and ends at frame 930. Only frames 900, 903, 905, 907, 909, 911, 913, 917, and 925 are shown.

The current work uses an appearance model given by one single multivariate Gaussian whose parameters are slowly updated over time. The robustness of this model is improved through the use of robust statistics that prevent outliers from deteriorating the global appearance model. This relatively simple model was adopted to allow real-time performance. We found that the tracking based on this model was successful even in the presence of occlusions caused by a rotated face and occluding hands. , 2004; Lee, 2005) and/or illumination templates to take into account sudden and significant local appearance changes due for instance to the presence of shadows.

2). 40 Affective Computing, Focus on Emotion Expression, Synthesis and Recognition Figure 17: The probability of each expression as a function of time associated with three low resolution videos. The right images displays the 25th frame of each video. Facial Expression Recognition in the Presence of Head Motion 41 Figure 18: Impact of noisy 3D head pose on the stochastic estimation of the facial actions. In each graph, the solid curve depicts the facial actions computed by the developed framework.

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