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Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions

Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activati...

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Detalles Bibliográficos
Autores principales: Gat, Liraz, Gerston, Aaron, Shikun, Liu, Inzelberg, Lilah, Hanein, Yael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863227/
https://www.ncbi.nlm.nih.gov/pubmed/35192638
http://dx.doi.org/10.1371/journal.pone.0262286
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author Gat, Liraz
Gerston, Aaron
Shikun, Liu
Inzelberg, Lilah
Hanein, Yael
author_facet Gat, Liraz
Gerston, Aaron
Shikun, Liu
Inzelberg, Lilah
Hanein, Yael
author_sort Gat, Liraz
collection PubMed
description Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis.
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spelling pubmed-88632272022-02-23 Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions Gat, Liraz Gerston, Aaron Shikun, Liu Inzelberg, Lilah Hanein, Yael PLoS One Research Article Computer vision (CV) is widely used in the investigation of facial expressions. Applications range from psychological evaluation to neurology, to name just two examples. CV for identifying facial expressions may suffer from several shortcomings: CV provides indirect information about muscle activation, it is insensitive to activations that do not involve visible deformations, such as jaw clenching. Moreover, it relies on high-resolution and unobstructed visuals. High density surface electromyography (sEMG) recordings with soft electrode array is an alternative approach which provides direct information about muscle activation, even from freely behaving humans. In this investigation, we compare CV and sEMG analysis of facial muscle activation. We used independent component analysis (ICA) and multiple linear regression (MLR) to quantify the similarity and disparity between the two approaches for posed muscle activations. The comparison reveals similarity in event detection, but discrepancies and inconsistencies in source identification. Specifically, the correspondence between sEMG and action unit (AU)-based analyses, the most widely used basis of CV muscle activation prediction, appears to vary between participants and sessions. We also show a comparison between AU and sEMG data of spontaneous smiles, highlighting the differences between the two approaches. The data presented in this paper suggests that the use of AU-based analysis should consider its limited ability to reliably compare between different sessions and individuals and highlight the advantages of high-resolution sEMG for facial expression analysis. Public Library of Science 2022-02-22 /pmc/articles/PMC8863227/ /pubmed/35192638 http://dx.doi.org/10.1371/journal.pone.0262286 Text en © 2022 Gat 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
Gat, Liraz
Gerston, Aaron
Shikun, Liu
Inzelberg, Lilah
Hanein, Yael
Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
title Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
title_full Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
title_fullStr Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
title_full_unstemmed Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
title_short Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
title_sort similarities and disparities between visual analysis and high-resolution electromyography of facial expressions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863227/
https://www.ncbi.nlm.nih.gov/pubmed/35192638
http://dx.doi.org/10.1371/journal.pone.0262286
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