Can a linear autoassociator recognize faces from new orientations?

An often noted limitation of computational models of faces operating on two-dimensional pixel intensity representations is that they cannot handle changes in orientation. We show that this limitation can be overcome by the use of multiple views of a given face instead of a single view to represent the face. Specifically, we show that a linear autoassociator trained to reconstruct multiple views of a set of faces is able to recognize the faces from new view angles. An analysis of the internal representation of the memory (i.e., eigenvectors of the between-unit-connection weight matrix) shows a dissociation between two kinds of perceptual information: orientation and identity information.