By vedran kordic
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Extra info for Affective Computing Focus on Emotion Expression Synthesis and Recognition
B show the tracking results associated with the same input frame. (a) displays the tracking results obtained with a particle filter adopting a singleclass dynamics. (b) displays the tracking results with our proposed approach adopting the six auto-regressive models. As can be seen, by using mixed states with learned multi-class dynamics, the facial action tracking becomes considerably more accurate (see the adaptation of the mouth region-the lower lip). Effect of rapid and/or discontinuous facial movements It is well known that facial expressions introduce rapid facial feature movements, and hence many developed trackers may fail to keep track of them.
Beliefs about emotions As noted above, facial expressions and gaze direction are not the only sources of emotion information transmitted by the face. Rather, since the appearance of the face tells us something about who the person is, it is reasonable to assume that this information will enter into our emotion judgments. We already know that individuals hold stereotypical beliefs about the emotions of others based on information such as their sex, their age, their culture, their status and their personality.
Active Contours. Springer-Verlag, 2000. V. Blanz and T. Vetter. Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-12, September 2003. M. Buenaposada, E. Munoz, and L. Baumela. Efficiently estimating facial expression and illumination in appearance-based tracking. In British Machine Vision Conference, 2006. P. Chandrasiri, T. Naemura, and H. Harashima. Interactive analysis and synthesis of facial expressions based on personal facial expression space.