Separating directional lighting variability in statistical face modelling based on texture space decomposition

In this paper we propose a simple method for decomposing the linear texture space of a facial appearance model into two linear subspaces, one for inter-individual variability and another for variations caused by directional changes of the lighting conditions. The approach used is to create one linear subspace from individuals with uniform illumination conditions and then filter a set of images with various directional lighting conditions by projecting corresponding textures on the previously built space; the residues are further used to build a second subspace for directional lighting. The resulted subspaces are orthogonal, so the overall texture model can be obtained by a simple concatenation of the two subspaces. The main advantage of this representation is that two sets of parameters are used to control inter-individual variation and separately intra-individual variation due to changes in illumination conditions.