TY - JOUR
T1 - Facial identity across the lifespan
AU - Mileva, Mila
AU - Young, Andrew W.
AU - Jenkins, Rob
AU - Burton, A. Mike
N1 - Funding Information:
This work was supported by the European Research Council under the European Union’s Seventh Framework Programme ( FP/2007-2013 )/ERC Grant Agreement n.323262 to A. Mike Burton.
Publisher Copyright:
© 2019 Elsevier Inc.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2020/2
Y1 - 2020/2
N2 - We can recognise people that we know across their lifespan. We see family members age, and we can recognise celebrities across long careers. How is this possible, despite the very large facial changes that occur as people get older? Here we analyse the statistical properties of faces as they age, sampling photos of the same people from their 20s to their 70s. Across a number of simulations, we observe that individuals’ faces retain some idiosyncratic physical properties across the adult lifespan that can be used to support moderate levels of age-independent recognition. However, we found that models based exclusively on image-similarity only achieved limited success in recognising faces across age. In contrast, more robust recognition was achieved with the introduction of a minimal top-down familiarisation procedure. Such models can incorporate the within-person variability associated with a particular individual to show a surprisingly high level of generalisation, even across the lifespan. The analysis of this variability reveals a powerful statistical tool for understanding recognition, and demonstrates how visual representations may support operations typically thought to require conceptual properties.
AB - We can recognise people that we know across their lifespan. We see family members age, and we can recognise celebrities across long careers. How is this possible, despite the very large facial changes that occur as people get older? Here we analyse the statistical properties of faces as they age, sampling photos of the same people from their 20s to their 70s. Across a number of simulations, we observe that individuals’ faces retain some idiosyncratic physical properties across the adult lifespan that can be used to support moderate levels of age-independent recognition. However, we found that models based exclusively on image-similarity only achieved limited success in recognising faces across age. In contrast, more robust recognition was achieved with the introduction of a minimal top-down familiarisation procedure. Such models can incorporate the within-person variability associated with a particular individual to show a surprisingly high level of generalisation, even across the lifespan. The analysis of this variability reveals a powerful statistical tool for understanding recognition, and demonstrates how visual representations may support operations typically thought to require conceptual properties.
UR - http://www.scopus.com/inward/record.url?scp=85076696465&partnerID=8YFLogxK
U2 - 10.1016/j.cogpsych.2019.101260
DO - 10.1016/j.cogpsych.2019.101260
M3 - Article
C2 - 31865002
AN - SCOPUS:85076696465
SN - 0010-0285
VL - 116
JO - Cognitive Psychology
JF - Cognitive Psychology
M1 - 101260
ER -