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Representation of facial identity includes expression variability.

Annabelle S Redfern1, Christopher P Benton1

  • 1School of Experimental Psychology, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK.

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|May 16, 2018
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Summary

Facial recognition involves learning how expressions change a person's face. This study shows expression variability is key to forming accurate face representations, impacting recognition accuracy.

Keywords:
Face perceptionFace representationFacial expressionsFacial identityVisual perception

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computer Vision

Background:

  • Understanding how the human brain forms and retrieves facial representations is crucial for cognitive science.
  • Existing models like exemplar and prototype theories offer partial explanations for facial recognition.
  • The role of dynamic facial expressions in forming stable identity representations remains an active area of research.

Purpose of the Study:

  • To investigate the contribution of expression variability to the formation of face representations.
  • To determine if learning faces with varying expressiveness impacts subsequent recognition performance.
  • To propose a refined model of face representation that incorporates expression dynamics.

Main Methods:

  • Participants learned novel identities from face images characterized by either low or high expressiveness.
  • Recognition performance was assessed using a test set of novel facial images.
  • Response times and accuracy were recorded to measure recognition efficiency and effectiveness.

Main Results:

  • Recognition accuracy after low expressiveness training was significantly modulated by image expressiveness, with more expressive images leading to slower responses.
  • Recognition after high expressiveness training showed minimal dependence on image expressiveness.
  • These findings challenge existing exemplar and prototype theories of face representation.

Conclusions:

  • Facial representations incorporate variability in expressions, suggesting a dynamic rather than static model of face perception.
  • Learning to recognize an individual involves understanding the range of expressions they exhibit.
  • A combined model of average and exemplar representations, preserving within-person variability, best explains the observed results.