Affective Computing Focus on Emotion Expression Synthesis by vedran kordic PDF

By vedran kordic

ISBN-10: 3902613238

ISBN-13: 9783902613233

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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.

Cohen, N. Sebe, A. Garg, L. S. Huang. Facial expression recognition from video sequences: Temporal and static modeling. , 91(1-2):160-187, 2003. F. Dornaika and F. Davoine. View- and texture-independent facial expression recognition in videos using dynamic programming. In IEEE International Conference on Image Processing, 2005. F. Dornaika and F. Davoine. On appearance based face and facial action tracking. IEEE Transactions on Circuits and Systems for Video Technology, 16(9), September 2006. P.

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.

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Affective Computing Focus on Emotion Expression Synthesis and Recognition by vedran kordic


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