Estimation of face parameters using correlation analysis and a topology preserving prior

Candide-3 is a well-known model, used to represent triangular meshes of human faces. It is common to only estimate 17 to 21 of the 79 model parameters. We show that these are insufficient to fit model vertices to facial feature points with low error and if more parameters are estimated, the model mesh deforms to unnatural configurations. To overcome this problem, we propose a novel solution: Given facial feature points, we propose to estimate the model parameters in subsets in which they are uncorrelated. Additionally we present a term to penalize topologically incorrect triangular mesh configurations. As a result the average mean squared error between facial feature points and model vertices is reduced by 90%, while face topology is preserved.