A Generative Model of Cultural Face Attractiveness

Jiayu Zhan, Meng Liu, Oliver G. B. Garrod, Rachael E. Jack, Philippe G. Schyns

Abstract: Equipping virtual agents with attractive face can facilitate human-machine interactions. Given the real-world diversity of human's aesthetic preference and the complexity of 3D face information that comprises high-dimensional shape and complexion variations, synthesizing attractive faces for virtual human is particularly challenging. Here, we addressed this challenge using a psychological data-driven approach that reverse engineers the attractive features perceived by human participants from the randomly generated faces that capture the nature distribution of multivariate 3D shape and 2D complexion variations in human population, separately for Western European and East Asian cultures. Our analysis revealed a set of features that can increase the attractiveness of faces in each culture, including their cultural idiosyncrasies. These human and culturally validated features can thus be synthesized on virtual agents to enhance their utility in practical applications.