Abstract
In this paper we provide the first, to the best of our knowledge, Bayesian formulation of one of the most successful and well-studied statistical models of shape and texture, i.e. Active Appearance Models (AAMs). To this end, we use a simple probabilistic model for texture generation assuming both Gaussian noise and a Gaussian prior over a latent texture space. We retrieve the shape parameters by formulating a novel cost function obtained by marginalizing out the latent texture space. This results in a fast implementation when compared to other simultaneous algorithms for fitting AAMs, mainly due to the removal of the calculation of texture parameters. We demonstrate that, contrary to what is believed regarding the performance of AAMs in generic fitting scenarios, optimization of the proposed cost function produces results that outperform discriminatively trained state-of-the-art methods in the problem of facial alignment "in the wild".