Affective Computing Focus on Emotion Expression Synthesis by vedran kordic

By vedran kordic

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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. 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.

J. Lyons, J. Budynek, and S. Akamatsu. Automatic classification of single facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(12):1357-1362, 1999. F. Moreno, A. Tarrida, J. Andrade-Cetto, and A. Sanfeliu. 3D real-time tracking fusing color histograms and stereovision. In IEEE International Conference on Pattern Recognition, 2002. 44 Affective Computing, Focus on Emotion Expression, Synthesis and Recognition B. North, A. Blake, M. Isard, and J. Rittscher. Learning and classification of complex dynamics.

Again, we use the training videos associated with the CMU database. In order to obtain trajectories with the same number of frames (duration) the trajectories belonging to the same expression class are aligned using the DTW technique. Recall that this technique allows a frame-to-frame correspondence between two time series. Let eij be the aligned trajectory i belonging to the expression class j. The example eij is represented by a column vector of dimension 1×6T and is obtained by concatenating the facial action 6-vectors τ a(t): 24 Affective Computing, Focus on Emotion Expression, Synthesis and Recognition Note that T represents the duration of the aligned trajectories which will be fixed for all examples.

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