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Additional resources for Affective Computing, Focus on Emotion Expression, Synthesis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12):1424-1445, 2000. P. Perez and J. Vermaak. Bayesian tracking with auxiliary discrete processes. application to detection and tracking of objects with occlusions. In IEEE ICCV Workshop on Dynamical Vision, Beijing, China, 2005. L. Rabiner and B. Juang. Fundamentals of Speech Recognition. J, Prentice Hall, 1993. Y. Tian, T. F. Cohn. Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:97-115, 2001.
Motion is modelled by a random noise. 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.
The upper part of this figure shows 9 frames of this sequence: 50, 130, 221, 300, 371, 450, 500, 620, and 740. The two plots illustrate the probability of each expression as a function of time (frames). The lower part of this figure shows the tracking results associated with frames 130, 371, and 450. The upper left corner of these frames depicts the appearance mean and the current shape-free facial patch. a illustrates the weighted average of the tracked facial actions, τˆ a(t). For the sake of clarity, only three out of six components are shown.