Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) for Improved User Engagement
arxiv(2024)
摘要
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.
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