Assessing the Big Five personality traits using real-life static facial images

There is ample evidence that a human face provides signals of human personality and behaviour. Previous studies have found associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations were found for conscientiousness (.360 for men and .335 for women), exceeding the results obtained in prior studies. The findings provide strong support for the hypothesis that it is possible to predict multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets.