Learning inhomogeneous Gibbs model of faces by minimax entropy
In this paper we propose a novel inhomogeneous Gibbs model by the minimax entropy principle, and apply it to face modeling. The maximum entropy principle generalizes the statistical properties of the observed samples and results in the Gibbs distribution, while the minimum entropy principle makes the learnt distribution close to the observed one. To capture the fine details of a face, an inhomogeneous Gibbs model is derived to learn the local statistics of facial feature paints. To alleviate the high dimensionality problem of face models, we propose to learn the distribution in a subspace reduced by principal component analysis or PCA. We demonstrate that our model effectively captures important and subtle non-Gaussian face patterns and efficiently generates good face models.