Abstract

In this paper, we constrain faces to points on a manifold within the parameter space of a linear statistical model. The manifold is the subspace of faces which have maximally likely distinctiveness and different points correspond to unique identities. We show how the tools of differential geometry can be used to replace linear operations such as warping and averaging with operations on the surface of this manifold. We use the manifold to develop a new method for fitting a statistical face shape model to data, which is both robust (avoids overfitting) and overcomes model dominance (is not susceptible to local minima close to the mean face). Our method outperforms a generic non-linear optimiser when fitting a dense 3D morphable face model to data.