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By Jimmy Or (Editor)

ISBN-10: 3902613238

ISBN-13: 9783902613233

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Since the 3D head pose ht is already computed, we are left with the mixed state at = [ τ aT(t ) ,γt]T. The dimension of the vector at is 7. Here we will employ a particle filter algorithm allowing the recursive estimation of the posterior distribution p (at⏐ x1:(t)) using a particle set. This is approximated by a set of J particles { (a t(0) ,w t(0) ),…, (a t( J ) ,w t( J ) )}. Once this distribution is known the facial actions as well as the expression can be inferred using some loss function such as the MAP or the mean.

Tian. Comprehensive database for facial expression analysis. In International Conference on Automatic Face and Gesture Recognition, pages 46-53, Grenoble, France, March 2000. D. Lee. Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):827-832, 2005. -K. Liao and I. Cohen. Classifying facial gestures in presence of head motion. In IEEE Workshop on Vision for Human-Computer Interaction, 2005. L. Ljung. System Identification: Theory for the User.

Niemann. A region-based method for model-free object tracking. In 16th International Conference on Pattern Recognition, 2002. J. Huber. Robust Statistics. Wiley, 1981. M. Isard and A. Blake. A mixed-state condensation tracker with automatic model switching. In Proc. IEEE International Conference on Computer Vision, 1998. D. J. F. El-Maraghi. Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10):12961311, 2003. T. Kanade, J. L.

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Affective Computing, Focus on Emotion Expression, Synthesis and Recognition by Jimmy Or (Editor)


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