Automatic extraction of the face identity-subspace
Facial variation divides into a number of functional subspaces, and variation unique to the ensemble. An improved method of measuring these is presented, within the space defined by an appearance model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by principal components analysis on appropriate groups of faces. A recoding algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble-specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those, which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.
appearance models principal components analysis face recognition dimensionality estimation