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Exploring facial expressions and action unit domains for Parkinson detection
BACKGROUND AND OBJECTIVE: Patients suffering from Parkinson’s disease (PD) present a reduction in facial movements called hypomimia. In this work, we propose to use machine learning facial expression analysis from face images based on action unit domains to improve PD detection. We propose different...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894465/ https://www.ncbi.nlm.nih.gov/pubmed/36730168 http://dx.doi.org/10.1371/journal.pone.0281248 |
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author | Gomez, Luis F. Morales, Aythami Fierrez, Julian Orozco-Arroyave, Juan Rafael |
author_facet | Gomez, Luis F. Morales, Aythami Fierrez, Julian Orozco-Arroyave, Juan Rafael |
author_sort | Gomez, Luis F. |
collection | PubMed |
description | BACKGROUND AND OBJECTIVE: Patients suffering from Parkinson’s disease (PD) present a reduction in facial movements called hypomimia. In this work, we propose to use machine learning facial expression analysis from face images based on action unit domains to improve PD detection. We propose different domain adaptation techniques to exploit the latest advances in automatic face analysis and face action unit detection. METHODS: Three different approaches are explored to model facial expressions of PD patients: (i) face analysis using single frame images and also using sequences of images, (ii) transfer learning from face analysis to action units recognition, and (iii) triplet-loss functions to improve the automatic classification between patients and healthy subjects. RESULTS: Real face images from PD patients show that it is possible to properly model elicited facial expressions using image sequences (neutral, onset-transition, apex, offset-transition, and neutral) with accuracy improvements of up to 5.5% (from 72.9% to 78.4%) with respect to single-image PD detection. We also show that our proposed action unit domain adaptation provides improvements of up to 8.9% (from 78.4% to 87.3%) with respect to face analysis. Finally, we also show that triplet-loss functions provide improvements of up to 3.6% (from 78.8% to 82.4%) with respect to action unit domain adaptation applied upon models created from scratch. The code of the experiments is available at https://github.com/luisf-gomez/Explorer-FE-AU-in-PD. CONCLUSIONS: Domain adaptation via transfer learning methods seem to be a promising strategy to model hypomimia in PD patients. Considering the good results and also the fact that only up to five images per participant are considered in each sequence, we believe that this work is a step forward in the development of inexpensive computational systems suitable to model and quantify problems of PD patients in their facial expressions. |
format | Online Article Text |
id | pubmed-9894465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98944652023-02-03 Exploring facial expressions and action unit domains for Parkinson detection Gomez, Luis F. Morales, Aythami Fierrez, Julian Orozco-Arroyave, Juan Rafael PLoS One Research Article BACKGROUND AND OBJECTIVE: Patients suffering from Parkinson’s disease (PD) present a reduction in facial movements called hypomimia. In this work, we propose to use machine learning facial expression analysis from face images based on action unit domains to improve PD detection. We propose different domain adaptation techniques to exploit the latest advances in automatic face analysis and face action unit detection. METHODS: Three different approaches are explored to model facial expressions of PD patients: (i) face analysis using single frame images and also using sequences of images, (ii) transfer learning from face analysis to action units recognition, and (iii) triplet-loss functions to improve the automatic classification between patients and healthy subjects. RESULTS: Real face images from PD patients show that it is possible to properly model elicited facial expressions using image sequences (neutral, onset-transition, apex, offset-transition, and neutral) with accuracy improvements of up to 5.5% (from 72.9% to 78.4%) with respect to single-image PD detection. We also show that our proposed action unit domain adaptation provides improvements of up to 8.9% (from 78.4% to 87.3%) with respect to face analysis. Finally, we also show that triplet-loss functions provide improvements of up to 3.6% (from 78.8% to 82.4%) with respect to action unit domain adaptation applied upon models created from scratch. The code of the experiments is available at https://github.com/luisf-gomez/Explorer-FE-AU-in-PD. CONCLUSIONS: Domain adaptation via transfer learning methods seem to be a promising strategy to model hypomimia in PD patients. Considering the good results and also the fact that only up to five images per participant are considered in each sequence, we believe that this work is a step forward in the development of inexpensive computational systems suitable to model and quantify problems of PD patients in their facial expressions. Public Library of Science 2023-02-02 /pmc/articles/PMC9894465/ /pubmed/36730168 http://dx.doi.org/10.1371/journal.pone.0281248 Text en © 2023 Gomez et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gomez, Luis F. Morales, Aythami Fierrez, Julian Orozco-Arroyave, Juan Rafael Exploring facial expressions and action unit domains for Parkinson detection |
title | Exploring facial expressions and action unit domains for Parkinson detection |
title_full | Exploring facial expressions and action unit domains for Parkinson detection |
title_fullStr | Exploring facial expressions and action unit domains for Parkinson detection |
title_full_unstemmed | Exploring facial expressions and action unit domains for Parkinson detection |
title_short | Exploring facial expressions and action unit domains for Parkinson detection |
title_sort | exploring facial expressions and action unit domains for parkinson detection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9894465/ https://www.ncbi.nlm.nih.gov/pubmed/36730168 http://dx.doi.org/10.1371/journal.pone.0281248 |
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