Customizable Avatars with Dynamic Facial Action Coded Expressions
  (CADyFACE) for Improved User Engagement
Megan A. Witherow Crystal Butler Winston J. Shields Furkan Ilgin Norou Diawara Janice Keener John W. Harrington Khan M. Iftekharuddin
Customizable 3D avatar-based facial expression stimuli may improve user
engagement in behavioral biomarker discovery and therapeutic intervention for
autism, Alzheimer's disease, facial palsy, and more. However, there is a lack
of customizable avatar-based stimuli with Facial Action Coding System (FACS)
action unit (AU) labels. Therefore, this study focuses on (1) FACS-labeled,
customizable avatar-based expression stimuli for maintaining subjects'
engagement, (2) learning-based measurements that quantify subjects' facial
responses to such stimuli, and (3) validation of constructs represented by
stimulus-measurement pairs. We propose Customizable Avatars with Dynamic Facial
Action Coded Expressions (CADyFACE) labeled with AUs by a certified FACS
expert. To measure subjects' AUs in response to CADyFACE, we propose a novel
Beta-guided Correlation and Multi-task Expression learning neural network
(BeCoME-Net) for multi-label AU detection. The beta-guided correlation loss
encourages feature correlation with AUs while discouraging correlation with
subject identities for improved generalization. We train BeCoME-Net for
unilateral and bilateral AU detection and compare with state-of-the-art
approaches. To assess construct validity of CADyFACE and BeCoME-Net, twenty
healthy adult volunteers complete expression recognition and mimicry tasks in
an online feasibility study while webcam-based eye-tracking and video are
collected. We test validity of multiple constructs, including face preference
during recognition and AUs during mimicry.