Jiaheng Su ¹ Marisabel Chang ² , ¹ USA, ² California State Polytechnic University, CA

Parkinsons disease diagnosis traditionally relies on subjective clinical evaluation and expensive medical equipment, resulting in prolonged wait times, substantial costs, and misdiagnosis affecting nearly 20% of cases. Many machine learning approaches require data from medical-grade imaging systems such as MRI, limiting accessibility. This paper presents a lightweight screening system utilizing facial movement analysis from standard video recordings to provide objective, accessible PD detection. The methodology processes videos through MediaPipe to generate facial mesh representations, extracting landmarks that are transformed into Action Unit features including eye aspect ratio, mouth aspect ratio, angles, velocity, and acceleration. A supervised Gradient Boosting classifier processes these features to distinguish PD patients from healthy controls. Experimental evaluation demonstrates 86.7% classification accuracy, substantially outperforming unsupervised K-Means clustering (46.7%). The proposed multi-region, dynamics-aware approach offers practical preliminary screening suitable for resource-limited clinical settings where specialist access remains constrained.

Parkinsons Disease, Facial Action Units, FaceMesh, Computer Vision, EAR and MAR.