Facial variation divides into a number of functional subspaces. An improved method of measuring these was designed, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity, expression) were obtained by Principal Components Analysis on appropriate groups of faces. An iterative algorithm was applied to image codings to maximize the probability of coding across these non-orthogonal subspaces before obtaining the projection on each sub-space and recalculating the spaces. This procedure enhances identity recognition, reduces overall sub-space variance and produces Principal Components with greater span and less contamination.