Modeling first impressions from highly variable facial images

Richard J W Vernon, Clare AM Sutherland, Andrew W. Young, Tom Hartley

Research output: Contribution to journalArticle

78 Citations (Scopus)
2 Downloads (Pure)

Abstract

First impressions of social traits, such as trustworthiness or dominance, are reliably perceived in faces, and despite their questionable validity they can have considerable real-world consequences. We sought to uncover the information driving such judgments, using an attribute-based approach. Attributes (physical facial features) were objectively measured from feature positions and colors in a database of highly variable "ambient" face photographs, and then used as input for a neural network to model factor dimensions (approachability, youthful-attractiveness, and dominance) thought to underlie social attributions. A linear model based on this approach was able to account for 58% of the variance in raters' impressions of previously unseen faces, and factor-attribute correlations could be used to rank attributes by their importance to each factor. Reversing this process, neural networks were then used to predict facial attributes and corresponding image properties from specific combinations of factor scores. In this way, the factors driving social trait impressions could be visualized as a series of computer-generated cartoon face-like images, depicting how attributes change along each dimension. This study shows that despite enormous variation in ambient images of faces, a substantial proportion of the variance in first impressions can be accounted for through linear changes in objectively defined features.

Original languageEnglish
Pages (from-to)E3353-E3361
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Volume111
Issue number32
Early online date28 Jul 2014
DOIs
Publication statusPublished - 12 Aug 2014

Keywords

  • face perception
  • social cognition
  • person perception
  • impression formation

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