Normalized velocity descriptors from facial keypoints with Random Forest yield 0.826 balanced accuracy and 0.855 AUROC on YouTubePD video classification, stable across 10 seeds with region ablation and permutation importance.
Facial Expression Analysis in Parkinsons’s Disease Using Machine Learning: A Review.ACM Computing Surveys, 57(8):1–25, 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Interpretable Temporal Facial-Region Motion Analysis for In-the-Wild Parkinson's Disease Video Classification
Normalized velocity descriptors from facial keypoints with Random Forest yield 0.826 balanced accuracy and 0.855 AUROC on YouTubePD video classification, stable across 10 seeds with region ablation and permutation importance.