We wish to model the way in which faces move in video sequences. We represent facial behaviour as a sequence of short actions. Each action is a sample from a statistical model representing the variability in the way it is performed. The ordering of actions is defined using a variable length Markov model. Action models and variable length Markov model are trained from a long (20000 frames) video sequence of a talking face. We propose a novel method of quantitatively evaluating the quality of the synthesis by measuring overlaps of parameter histograms. We apply this method to compare our technique with an alternative model that uses an autoregressive process.
|Title of host publication
|Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition|Proc. Sixth IEEE Int. Conf. Autom. Face Gest. Recogn.
|IEEE Computer Society
|Number of pages
|Published - 2004
|Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004 - Seoul
Duration: 1 Jul 2004 → …
|Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition FGR 2004
|1/07/04 → …