Facial expression-based hypomimia detection for Parkinson’s disease diagnosis: A static-dynamic mixed feature approach

Objective:

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder primarily affecting individuals over 65, poses diagnostic challenges due to its complex symptoms. This study aims to detect hypomimia, a characteristic PD symptom, by analyzing static and dynamic facial features from patients performing various facial expressions.

Methods:

Our method integrates static and dynamic facial features to facilitate PD auxiliary diagnosis. For static features, we conduct the similarity comparison in performing happy expressions between PD patients and healthy individuals utilizing a generative network. Subsequently, facial expression completion is assessed through the analysis of static facial images. For dynamic features, we conduct dynamic analysis by examining the patients’ facial movements, particularly focusing on eyelid and perioral movements in the expression videos. These features are processed through a specialized static-dynamic feature fusion network, enabling precise discrimination of PD. The integration of static and dynamic features is a novel aspect of our study.

Results:

The proposed method achieves a prediction accuracy (0.94) and recall (0.97), outperforming existing in-vitro diagnostic techniques due to its comprehensive analysis of facial expressions. To address data scarcity, we compiled Parkinson’s Disease Facial Expression Videos (PD-FEV) dataset, offering a valuable resource on facial expression analysis for PD diagnosis.

Conclusion:

This study enhances PD diagnosis by introducing an innovative approach to hypomimia detection through the integration of static and dynamic features, providing improved diagnostic accuracy and greater convenience for patients. Additionally, the PD-FEV dataset offers valuable data resources, advancing PD diagnosis in clinical practice.