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Understanding facial impressions between and within identities
Mila Mileva1, Andrew W Young1, Robin S S Kramer1
1Department of Psychology, University of York, UK.
Cognition
|May 19, 2019
Summary
Visual image statistics accurately predict human judgments of facial traits like trustworthiness and attractiveness. This explains why we consistently judge traits but struggle with unfamiliar face identity.
Area of Science:
- Cognitive Psychology
- Computer Vision
- Social Neuroscience
Background:
- Face perception research reveals a paradox: inconsistent identity judgments but consistent trait judgments for unfamiliar faces.
- Understanding the basis of consistent trait perception is crucial for face perception theories.
Purpose of the Study:
- To investigate whether visual image statistics of faces can predict consensual trait judgments.
- To determine the role of shape and surface properties in predicting trait impressions.
Main Methods:
- Analysis of visual image statistics from everyday ambient face images.
- Statistical modeling to correlate image properties with human judgments of trustworthiness, attractiveness, and dominance.
- Testing generalizability of models across different images of the same face and across different individuals.
Main Results:
- Image statistics predicted 51% of the variance in consensual trait impressions of novel faces.
- Shape properties were stronger predictors than surface properties; combined properties yielded best results.
- Models showed generalization across different images of the same face and between different individuals' faces.
Conclusions:
- Visual image statistics provide a significant basis for consensual trait judgments of faces.
- Idiosyncratic variations in faces hinder identity recognition but not trait perception.
- Simple operational models based on image statistics can capture consistencies in face trait perception.
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