  Carefully standardized facial images of 591 participants were taken in the
laboratory, while controlling for self-presentation, facial expression, head
orientation, and image properties. They were presented to human raters and a
facial recognition algorithm: both humans (r=.21) and the algorithm (r=.22)
could predict participants' scores on a political orientation scale (Cronbach's
alpha=.94) decorrelated with age, gender, and ethnicity. These effects are on
par with how well job interviews predict job success, or alcohol drives
aggressiveness. Algorithm's predictive accuracy was even higher (r=.31) when it
leveraged information on participants' age, gender, and ethnicity. Moreover,
the associations between facial appearance and political orientation seem to
generalize beyond our sample: The predictive model derived from standardized
images (while controlling for age, gender, and ethnicity) could predict
political orientation (r=.13) from naturalistic images of 3,401 politicians
from the U.S., UK, and Canada. The analysis of facial features associated with
political orientation revealed that conservatives tended to have larger lower
faces. The predictability of political orientation from standardized images has
critical implications for privacy, the regulation of facial recognition
technology, and understanding the origins and consequences of political
orientation.
