By vedran kordic
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Additional resources for Affective Computing Focus on Emotion Expression Synthesis and Recognition
1 Facial action dynamic models Corresponding to each basic expression class, γ, there is a stochastic dynamic model describing the temporal evolution of the facial actions τ a(t), given the expression. It is assumed to be a Markov model of order K. For each basic expression γ, we associate a Gaussian Auto-Regressive Process defined by: (16) in which wt is a vector of 6 independent random N(0, 1) variables. The parameters of the dynamic model are: (i) deterministic parameters A1γ , A2γ ,…, AKγ and d γ, and stochastic parameters Bγ which are multipliers for the stochastic process wt.
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IEEE International Conference on Computer Vision, 1998. D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In AAAI-94 Workshop on Knowledge Discovery in Databases, 1994. A. Blake and M. Isard. 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.
Affective Computing Focus on Emotion Expression Synthesis and Recognition by vedran kordic