FICAug: Feature-Informed Clustering and Augmentation for Facial-Expression-Based Parkinson's Disease Screening

תקציר מקורי באנגליתarXiv:2409.17685v3 Announce Type: replace-cross Abstract: Hypomimia has drawn growing interest as a digital marker for screening Parkinson's disease (PD). However, developing reliable facial-expression-based screening models is challenging because clinical PD datasets are small, exposing models to only a narrow range of how hypomimia can appear across individuals. Standard augmentation strategies do not solve this problem; recombining or perturbing existing samples produces variation, but not new facial configurations that are plausible and clinically meaningful. We introduce FICAug to address this gap. The framework clusters Action Unit (AU) feature vectors extracted from facial expression images, discards clusters that mix labels inconsistently, and generates synthetic AU vectors within