Facial Expression Analysis in Parkinsons's Disease Using Machine Learning: A Review

Author(s)

Oliveira, Guilherme ; Ngo, Quoc ; Passos, Leandro ; Jodas, Danilo ; Papa, Joao ; Kumar, Dinesh

• Document type: Journal article
• Source: ACM COMPUTING SURVEYS; v. 57, n. 8, p. 25-pg., 2025-08-01.

Abstract

Computerised facial expression analysis is performed for a range of social and commercial applications and more recently its potential in medicine such as to detect Parkinson's Disease (PD) is emerging. This has possibilities for use in telehealth and population screening. The advancement of facial expression analysis using machine learning is relatively recent, with a majority of the published work being post-2019. We have performed a systematic review of the English-based publication on the topic from 2019 to 2024 to capture the trends and identify research opportunities that will facilitate the translation of this technology for recognising Parkinson's disease. The review shows significant advancements in the field, with facial expressions emerging as a potential biomarker for PD. Different machine learning models, from shallow to deep learning, could detect PD faces. However, the main limitation is the reliance on limited datasets. Furthermore, while significant progress has been made, model generalization must be tested before clinical applications. (AU)

• FAPESP's process: 23/10823-6 - On the Study and Development of Biological Plausible Computational Intelligent Models
• Grantee: Leandro Aparecido Passos Junior
• Support Opportunities: Scholarships in Brazil - Support Program for Fixating Young Doctors
• FAPESP's process: 23/14197-2 - HIDE - HypomImia DetEction using lightweight architectures
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
• Grantee: Daniel Carlos Guimarães Pedronette
• Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
• FAPESP's process: 19/02205-5 - Adversarial learning in natural language processing
• Grantee: Gustavo Henrique de Rosa
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 19/00585-5 - Evolutionary Generative Adversarial Networks applied to computer-assisted diabetic retinopathy diagnosis
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants