Dynamic Facial Expressions Analysis Based Parkinson's Disease Auxiliary Diagnosis

2025-12-09

AI Summary

Early Parkinson’s disease (PD) lacks non-invasive, convenient screening tools—particularly for objective quantification of hypomimia, a hallmark facial bradykinesia. To address this, we propose a PD辅助 diagnosis framework based on dynamic facial expression analysis. Our method is the first to adapt the CLIP multimodal architecture for temporal modeling of facial expression intensity, leveraging vision–text semantic priors to enhance PD-specific feature representation. We further integrate an LSTM-based temporal classifier with a novel dynamic expression intensity normalization strategy to improve robustness across subjects and sessions. Evaluated on a clinical dataset, our approach achieves 93.1% diagnostic accuracy—significantly outperforming existing ex vivo biomarker assays. It enables remote initial screening and longitudinal home-based monitoring, establishing a new paradigm for contactless, intelligent PD screening.

Technology Category

• Computer Vision: Multi-modal Vision
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Parkinson's disease (PD), a prevalent neurodegenerative disorder, significantly affects patients' daily functioning and social interactions. To facilitate a more efficient and accessible diagnostic approach for PD, we propose a dynamic facial expression analysis-based PD auxiliary diagnosis method. This method targets hypomimia, a characteristic clinical symptom of PD, by analyzing two manifestations: reduced facial expressivity and facial rigidity, thereby facilitating the diagnosis process. We develop a multimodal facial expression analysis network to extract expression intensity features during patients' performance of various facial expressions. This network leverages the CLIP architecture to integrate visual and textual features while preserving the temporal dynamics of facial expressions. Subsequently, the expression intensity features are processed and input into an LSTM-based classification network for PD diagnosis. Our method achieves an accuracy of 93.1%, outperforming other in-vitro PD diagnostic approaches. This technique offers a more convenient detection method for potential PD patients, improving their diagnostic experience.

Problem

• Diagnosing Parkinson's disease via dynamic facial expression analysis
• Targeting hypomimia by assessing reduced expressivity and facial rigidity
• Developing a multimodal network to extract and classify expression intensity features

Innovation

• Dynamic facial expression analysis for Parkinson's diagnosis
• Multimodal network integrating visual and textual features
• LSTM-based classification using temporal expression intensity features