Sarah D. Gunnery

Perceptions of Duchenne and non-Duchenne smiles: A meta-analysis.

A meta-analysis was conducted to compare perceptions of Duchenne smiles, smiles that include activation of the cheek raiser muscle that creates crows feet around the eyes, with perceptions of non-Duchenne smiles, smiles without cheek raiser activation. In addition to testing the overall effect, moderator analyses were conducted to test how methodological, stimulus-specific and perceiver-specific differences between studies predicted the overall effect size. The meta-analysis found that, overall, Duchenne smiles and people producing Duchenne smiles are rated more positively (i.e., authentic, genuine, real, attractive, trustworthy) than non-Duchenne smiles and people producing non-Duchenne smiles. The difference between Duchenne and non-Duchenne smiles was greater when the stimuli were videos rather than photographs, when smiles were elicited naturally rather than through posing paradigms and when Duchenne and non-Duchenne smiles were not matched for intensity of the lip corner puller in addition to other perceiver and methodological moderators.

Mapping spontaneous facial expression in people with Parkinson’s disease: A multiple case study design

Abstract People with Parkinson’s disease (PD) often experience a decrease in their facial expressivity, but little is known about how the coordinated movements across regions of the face are impaired in PD. The face has neurologically independent regions that coordinate to articulate distinct social meanings that others perceive as gestalt expressions, and so understanding how different regions of the face are affected is important. Using the Facial Action Coding System, this study comprehensively measured spontaneous facial expression across 600 frames for a multiple case study of people with PD who were rated as having varying degrees of facial expression deficits, and created correlation matrices for frequency and intensity of produced muscle activations across different areas of the face. Data visualization techniques were used to create temporal and correlational mappings of muscle action in the face at different degrees of facial expressivity. Results showed that as severity of facial expression deficit increased, there was a decrease in number, duration, intensity, and coactivation of facial muscle action. This understanding of how regions of the parkinsonian face move independently and in conjunction with other regions will provide a new focus for future research aiming to model how facial expression in PD relates to disease progression, stigma, and quality of life.

Predicting Active Facial Expressivity in People with Parkinson's Disease

Our capacity to engage in meaningful conversations depends on a multitude of communication signals, including verbal delivery of speech, tone and modulation of voice, execution of body gestures, and exhibition of a range of facial expressions. Among these cues, the expressivity of the face strongly indicates the level of ones engagement during a social interaction. It also significantly influences how others perceive ones personality and mood. Individuals with Parkinsons disease whose facial muscles have become rigid have difficulty exhibiting facial expressions. In this work, we investigate how to computationally predict an accurate and objective score for facial expressivity of a person. We present a method that computes geometric shape features of the face and predicts a score for facial expressivity. Our method trains a random forest regressor based on features extracted from a set of training videos of interviews of people suffering from Parkinsons disease. We evaluated our formulation on a dataset of 727 20-second video clips using 9-fold cross validation. We also provide insight on the geometric features that are important in this prediction task by computing variable importance scores for our features. Our results suggest that the dynamics of the eyes and eyebrows are better predictors of facial expressivity than dynamics of the mouth.

The Expression and Perception of the Duchenne Smile

The smile, as a nonverbal behavior, can be a quite confusing expression. People smile for many reasons and when experiencing many different emotions including embarrassment, anger, jealousy, and distress along with many kinds of positive affect (Ekman & Friesen, 1982; Keltner, 1995; Ansfield, 2007; Ambadar et al., 2009). Although people smile when they are feeling a range of different emotions, the smile is largely synonymous with happiness, and people are very good at perceiving when another person is feeling happy rather than one of the other emotions listed above.