FICAug: Feature-Informed Clustering and Augmentation for Facial-Expression-Based Parkinson's Disease Screening
TL;DR
The results suggest that synthetic image generation, when guided by label consistency and clinical feature structure, can function as an effective intermediate representation-learning step for facial-expression-based PD screening in settings where clinical data remain scarce.
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 the retained clusters with Gaussian sampling. GANimation then reconstructs these synthetic vectors into realistic facial images. A ResNet18 model is pretrained on these reconstructed images, and then fine-tuned on real clinical data. Using the UT-MoDaPark dataset, FICAug achieved 88.63% cross-validation accuracy and 94.00% test accuracy, outperforming both a standard ResNet18 baseline and self-supervised alternatives, including DINO and MSN. These results suggest that synthetic image generation, when guided by label consistency and clinical feature structure, can function as an effective intermediate representation-learning step for facial-expression-based PD screening in settings where clinical data remain scarce.
