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Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding

Although electromyography (EMG) remains the standard, researchers have begun using automated facial action coding system (FACS) software to evaluate spontaneous facial mimicry despite the lack of evidence of its validity. Using the facial EMG of the zygomaticus major (ZM) as a standard, we confirmed...

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Autores principales: Hsu, Chun-Ting, Sato, Wataru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675524/
https://www.ncbi.nlm.nih.gov/pubmed/38005462
http://dx.doi.org/10.3390/s23229076
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author Hsu, Chun-Ting
Sato, Wataru
author_facet Hsu, Chun-Ting
Sato, Wataru
author_sort Hsu, Chun-Ting
collection PubMed
description Although electromyography (EMG) remains the standard, researchers have begun using automated facial action coding system (FACS) software to evaluate spontaneous facial mimicry despite the lack of evidence of its validity. Using the facial EMG of the zygomaticus major (ZM) as a standard, we confirmed the detection of spontaneous facial mimicry in action unit 12 (AU12, lip corner puller) via an automated FACS. Participants were alternately presented with real-time model performance and prerecorded videos of dynamic facial expressions, while simultaneous ZM signal and frontal facial videos were acquired. Facial videos were estimated for AU12 using FaceReader, Py-Feat, and OpenFace. The automated FACS is less sensitive and less accurate than facial EMG, but AU12 mimicking responses were significantly correlated with ZM responses. All three software programs detected enhanced facial mimicry by live performances. The AU12 time series showed a roughly 100 to 300 ms latency relative to the ZM. Our results suggested that while the automated FACS could not replace facial EMG in mimicry detection, it could serve a purpose for large effect sizes. Researchers should be cautious with the automated FACS outputs, especially when studying clinical populations. In addition, developers should consider the EMG validation of AU estimation as a benchmark.
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spelling pubmed-106755242023-11-09 Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding Hsu, Chun-Ting Sato, Wataru Sensors (Basel) Article Although electromyography (EMG) remains the standard, researchers have begun using automated facial action coding system (FACS) software to evaluate spontaneous facial mimicry despite the lack of evidence of its validity. Using the facial EMG of the zygomaticus major (ZM) as a standard, we confirmed the detection of spontaneous facial mimicry in action unit 12 (AU12, lip corner puller) via an automated FACS. Participants were alternately presented with real-time model performance and prerecorded videos of dynamic facial expressions, while simultaneous ZM signal and frontal facial videos were acquired. Facial videos were estimated for AU12 using FaceReader, Py-Feat, and OpenFace. The automated FACS is less sensitive and less accurate than facial EMG, but AU12 mimicking responses were significantly correlated with ZM responses. All three software programs detected enhanced facial mimicry by live performances. The AU12 time series showed a roughly 100 to 300 ms latency relative to the ZM. Our results suggested that while the automated FACS could not replace facial EMG in mimicry detection, it could serve a purpose for large effect sizes. Researchers should be cautious with the automated FACS outputs, especially when studying clinical populations. In addition, developers should consider the EMG validation of AU estimation as a benchmark. MDPI 2023-11-09 /pmc/articles/PMC10675524/ /pubmed/38005462 http://dx.doi.org/10.3390/s23229076 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hsu, Chun-Ting
Sato, Wataru
Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding
title Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding
title_full Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding
title_fullStr Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding
title_full_unstemmed Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding
title_short Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding
title_sort electromyographic validation of spontaneous facial mimicry detection using automated facial action coding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675524/
https://www.ncbi.nlm.nih.gov/pubmed/38005462
http://dx.doi.org/10.3390/s23229076
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