Identity From Variation: Representations of Faces Derived From Multiple Instances

TY - JOUR

T1 - Identity From Variation

T2 - Representations of Faces Derived From Multiple Instances

AU - Burton, A Mike

AU - Kramer, Robin S S

AU - Ritchie, Kay L

AU - Jenkins, Rob

N1 - Copyright © 2015 Cognitive Science Society, Inc. All rights reserved. This is the author produced peer reviewed version of the following article: Burton, A. M., Kramer, R. S. S., Ritchie, K. L. and Jenkins, R. (2015), Identity From Variation: Representations of Faces Derived From Multiple Instances. Cognitive Science, which has been published in final form at DOI: 10.1111/cogs.12231. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Uploaded in accordance with the publisher's self-archiving policy.

PY - 2016/1/10

Y1 - 2016/1/10

N2 - 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.

AB - 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.

KW - Face learning

KW - Face recognition

KW - Familiarity

KW - Principal components analysis

KW - Variability

KW - Learning

KW - Facial Recognition

KW - Humans

KW - Principal Component Analysis

UR - http://www.scopus.com/inward/record.url?scp=84947470540&partnerID=8YFLogxK

U2 - 10.1111/cogs.12231

DO - 10.1111/cogs.12231

M3 - Article

C2 - 25824013

SN - 0364-0213

VL - 40

SP - 202

EP - 223

JO - COGNITIVE SCIENCE

JF - COGNITIVE SCIENCE

IS - 1

ER -