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FGR
2004
IEEE

Particle Filtering with Factorized Likelihoods for Tracking Facial Features

10 years 6 months ago
Particle Filtering with Factorized Likelihoods for Tracking Facial Features
In the recent years particle filtering has been the dominant paradigm for tracking facial and body features, recognizing temporal events and reasoning in uncertainty. A major problem associated with it is that its performance deteriorates drastically when the dimensionality of the state space is high. In this paper, we address this problem when the state space can be partitioned in groups of random variables whose likelihood can be independently evaluated. We introduce a novel proposal density which is the product of the marginal posteriors of the groups of random variables. The proposed method requires only that the interdependencies between the groups of random variables (i.e. the priors) can be evaluated and not that a sample can be drawn from them. We adapt our scheme to the problem of multiple template-based tracking of facial features. We propose a color-based observation model that is invariant to changes in illumination intensity. We experimentally show that our algorithm clea...
Ioannis Patras, Maja Pantic
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where FGR
Authors Ioannis Patras, Maja Pantic
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