Modeling first impressions from highly variable facial images

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Abstract
Significance: 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.

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