Stability from Variation: The Case of Face Recognition the M.D. Vernon Memorial Lecture

Abstract

A theme running through M.D. Vernon's discussions of visual perception was the key question of how we perceive a stable world despite continuous variation. The central problem in face identification is how we build stable representations from exemplars that vary, both rigidly and non-rigidly, from instant to instant and from encounter to encounter. Experiments reveal that people are rather poor at generalizing from one exemplar of a face to another (e.g. from one photograph to another showing a different view or expression) yet highly accurate at encoding precise details of faces within the range shown by several slightly different exemplars. Moreover, provided instructions do not encourage subjects explicitly to attend to the way that different exemplars vary, faces are retained in a way that enhances familiarity of the prototype of the set, even if this was not presented for study. It is suggested that our usual encounters with continuous variations of facial expressions, angles, and lightings provide the conditions necessary to establish stable representations of individuals within an overall category (the face) where all members share the same overall structure. These observations about face recognition would probably not have come as any great surprise to Maggie Vernon, many of whose more general observations about visual perception anticipated such conclusions.