BAYESIAN FACES VIA HIERARCHICAL TEMPLATE MODELING

We consider the problem of directly extracting high-level shape inform ation from images of scenes involving faces. The approach adopted owes much to the work of Grenander and colleagues at Brown University on p attern analysis and involves designing stochastic deformable templates for objects in the underlying image scenes. A wide range of realistic object poses can be captured by imposing a prior probability distribu tion over the space of allowable deformations. We show how hierarchica l models can be used to organize the prior information into a coherent structure. Markov chain Monte Carlo methods are exploited to recover the deformation given observed image data.