Proceedings of the Annual Meeting of the Cognitive Science Society

Humans make complex inferences on faces, ranging from ob-jective properties (gender, ethnicity, expression, age, identity,etc) to subjective judgments (facial attractiveness, trustworthi-ness, sociability, friendliness, etc). While the objective as-pects of face perception have been extensively studied, rela-tively fewer computational models have been developed forthe social impressions of faces. Bridging this gap, we de-velop a method to predict human impressions of faces in 40subjective social dimensions, using deep representations fromstate-of-the-art neural networks. We find that model perfor-mance grows as the human consensus on a face trait increases,and that model predictions outperform human groups in cor-relation with human averages. This illustrates the learnabilityof subjective social perception of faces, especially when thereis high human consensus. Our system can be used to decidewhich photographs from a personal collection will make thebest impression. The results are significant for the field of so-cial robotics, demonstrating that robots can learn the subjectivejudgments defining the underlying fabric of human interaction.