Optimal faces for gender and expression: a new technique for measuring dynamic templates used in face perception.

Facial expressions are important for human communications. Face perception studies often measure the impact of major degradation (e.g., noise, inversion, short presentations, masking, alterations) on natural expression recognition performance. Here, we introduce a novel face perception technique using rich and undegraded stimuli. Participants modified faces to create optimal representations of given expressions. Using sliders, participants adjusted 53 face components (including 37 dynamic) including head, eye, eyebrows, mouth, and nose shape and position. Data was collected from six participants and 10 conditions (six emotions + pain + gender + neutral). Some expressions had unique features (e.g., frown for anger, upward-curved mouth for happiness), whereas others had shared features (e.g., open eyes and mouth for surprise and fear). Happiness was different from other emotions. Surprise was different from other emotions except fear. Weighted sum morphing provides acceptable stimuli for gender-neutral and dynamic stimuli. Many features were correlated, including (1) head size with internal feature sizes as related to gender, (2) internal feature scaling, and (3) eyebrow height and eye openness as related to surprise and fear. These findings demonstrate the method's validity for measuring the optimal facial expressions, which we argue is a more direct measure of their internal representations.