Testing Viewpoint Invariance in the Neural Representation of Faces: An MEG Study

Abstract

The aim of this study was to determine the extent to which the neural representation of faces in the visual cortex is viewpoint invariant. MEG was used to measure evoked responses to faces during an adaptation paradigm. Using familiar and unfamiliar faces, we compared the amplitude of the M170 response to repeated images of the same face compared to images of different faces. We found a reduction in the M170 amplitude to repeated presentations of the same face image compared to images of different faces when shown from the same viewpoint. To establish if this adaptation to the identity of a face was invariant to changes in viewpoint, we varied the viewing angle of the face within a block. In order to exert strict control over the viewpoint from which the face was viewed, we used 3D models recovered from single images using shape-from-shading. This makes the study unique in its use of techniques from machine vision in order to test human visual processes. We found a reduction in response was no longer evident when images of the same face were shown from different viewpoints. These results imply that the face-selective M170 response either reflects an early stage of face processing or that the computations underlying face recognition depend on a viewpoint-dependent neuronal representation.