Modelling Culturally Diverse Smiles Using Data-Driven Methods

Chaona Chen, Oliver G. B. Garrod, Philippe G. Schyns, Rachael E. Jack

Abstract: Smiling faces are often preferred in daily social interactions. Many socially interactive human-like virtual agents are equipped with the capability to produce standardized smiles that are widely considered to be universal. However, mounting evidence shows that people from different cultures prefer different smiles. To engage a culturally diverse range of human users, socially interactive human-like virtual agents must be equipped with culturally-valid dynamic facial expressions. To develop culturally sensitive smiles, we use data-driven, perception-based methods to model the facial expressions of happy in 60 individuals in two distinct cultures (East Asian and Western European). On each experimental trial, we generated a random facial animation composed of a random sub-set of individual face movements (i.e., AUs), each with a random movement. Each cultural participant categorized 2400 such facial animations according to an emotion label (e.g., happy) if appropriate, otherwise selecting ‘other.’ We derived facial expression models of happy for each cultural participant by measuring the statistical relationship between the dynamic AUs presented on each trial and each participant's responses. Analysis of the facial expression models revealed clear cross-cultural similarity and diversity in smiles–for example, smiling with raised cheeks (AU12-6) is culturally common, while open-mouth smiling (AU25-12) is Western-specific and smiling with eyebrow raising (AU1-2) is East Asian-specific. Analysis of the temporal dynamics of each AU further revealed cultural diversity in smiles. We anticipate that our approach will improve the social signalling capabilities of socially interactive human-like virtual agents and broaden their usability in global market.