A model based on 65 physical features was used to predict how people make snap judgements about a person's character. The paper in PNAS is outside the paywall.
Modeling first impressions from highly variable facial images
Richard J. W. Vernon, Clare A. M. Sutherland, Andrew W. Young, and Tom Hartley
Department of Psychology, University of York, Heslington, York YO10 5DD, United Kingdom
Understanding how first impressions are formed to faces is a topic of major theoretical and practical interest that has been given added importance through the widespread use of images of faces in social media. We create a quantitative model that can predict first impressions of previously unseen ambient images of faces (photographs reflecting the variability encountered in everyday life) from a linear combination of facial attributes, explaining 58% of the variance in raters’ impressions despite the considerable variability of the photographs. Reversing this process, we then demonstrate that face-like images can be generated that yield predictable social trait impressions in naive raters because they capture key aspects of the systematic variation in the relevant physical features of real faces.
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.