Decision Theoretic Modeling of Human Facial Displays

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Decision Theoretic Modeling of Human Facial Displays
We present a vision based, adaptive, decision theoretic model of human facial displays in interactions. The model is a partially observable Markov decision process, or POMDP. A POMDP is a stochastic planner used by an agent to relate its actions and utility function to its observations and to other context. Video observations are integrated into the POMDP using a dynamic Bayesian network that creates spatemporal abstractions of the input sequences. The parameters of the model are learned from training data using an a-posteriori constrained optimization technique based on the expectation-maximization algorithm. The training does not require facial display labels on the training data. The learning process discovers clusters of facial display sequences and their relationship to the context automatically. This avoids the need for human intervention in training data collection, and allows the models to be used without modification for facial display learning in any context without prior kno...
Jesse Hoey, James J. Little
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Jesse Hoey, James J. Little
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