We present an analysis of the effects of face distinctiveness on the performance of a computational model of recognition over viewpoint change. In the first stage of the model, the face stimulus is normalized by being mapped to an arbitrary standard view. In the second stage, the normalized stimulus is mapped into a ``face space'' spanned by a number of reference faces, and is classified as familiar or unfamiliar. We carried out experiments employing a parametrically generated family of face stimuli that vary in distinctiveness. The experiments show that while the ``view-mapping'' process operates more accurately for typical versus distinctive faces, the base level distinctiveness of the faces is preserved in the face space coding. These data provide insight into how the psychophysically well-established inverse relationship between the typicality and recognizability of faces might operate for recognition across changes in viewpoint.