Identity from variation: representations of faces derived from multiple instances

Burton, A. Mike and Kramer, Robin S. S. and Ritchie, Kay L. and Jenkins, Rob (2016) Identity from variation: representations of faces derived from multiple instances. Cognitive Science, 40 (1). pp. 202-223. ISSN 0364-0213

Full content URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/co...

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Abstract

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.

Additional Information:The final published version can be accessed online at https://onlinelibrary.wiley.com/doi/abs/10.1111/cogs.12231
Keywords:Face Perception, JCNotOpen
Subjects:C Biological Sciences > C800 Psychology
C Biological Sciences > C830 Experimental Psychology
Divisions:College of Social Science > School of Psychology
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ID Code:32395
Deposited On:29 Oct 2018 10:00

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