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Abstract
Understanding demographic difference in facial expression of happiness has crucial implications on social communication. However, prior research on facial emotion expression has mostly focused on the effect of a single demographic factor (typically gender, race, or age), and is limited by the small image dataset collected in laboratory settings. First, we used 30,000 (4800 after pre-processing) real-world facial images from Flickr, to analyze the facial expression of happiness as indicated by the intensity level of two distinctive facial action units, the Cheek Raiser (AU6) and the Lip Corner Puller (AU12), obtained automatically via a deep learning algorithm that we developed, after training on 75,000 images. Second, we conducted a statistical analysis on the intensity level of happiness, with both the main effect and the interaction effect of three core demographic factors on AU12 and AU6. Our results show that females generally display a higher AU12 intensity than males. African Americans tend to exhibit a higher AU6 and AU12 intensity, when compared with Caucasians and Asians. The older age groups, especially the 40–69-year-old, generally display a stronger AU12 intensity than the 0–3-year-old group. Our interdisciplinary study provides a better generalization and a deeper understanding on how different gender, race and age groups express the emotion of happiness differently.
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Details
1 The University of Hong Kong, Department of Electrical and Electronic Engineering, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757)