Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos (...) of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework. (shrink)

This article describes some differences between familiar and unfamiliar face processing. It presents the evidence that unfamiliar face recognition is poor. Since this poor performance has implications both practically and theoretically, it is important to establish the facts. The article analyses reasons that people appear to have little insight into their own poor performance with unfamiliar faces, and some sectors of society seem so keen to use faces as a means of proving identity. It reviews some historical research comparing familiar (...) and unfamiliar face processing. The study shows evidence for the assertion that despite eliminating the memory-load of normal eyewitness situations, it turns out that people are surprisingly bad at matching two images of the same unfamiliar person. Finally, it suggests that the modern tendency to conflate familiar and unfamiliar face processing, and to theorize about “face recognition” in general, lies at the heart of practical failures in this field. (shrink)