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Quantifying the efficacy of an automated facial coding software using videos of parents

INTRODUCTION: This work explores the use of an automated facial coding software - FaceReader - as an alternative and/or complementary method to manual coding. METHODS: We used videos of parents (fathers, n = 36; mothers, n = 29) taken from the Avon Longitudinal Study of Parents and Children. The vid...

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Autores principales: Burgess, R., Culpin, I., Costantini, I., Bould, H., Nabney, I., Pearson, R. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425266/
https://www.ncbi.nlm.nih.gov/pubmed/37583610
http://dx.doi.org/10.3389/fpsyg.2023.1223806
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author Burgess, R.
Culpin, I.
Costantini, I.
Bould, H.
Nabney, I.
Pearson, R. M.
author_facet Burgess, R.
Culpin, I.
Costantini, I.
Bould, H.
Nabney, I.
Pearson, R. M.
author_sort Burgess, R.
collection PubMed
description INTRODUCTION: This work explores the use of an automated facial coding software - FaceReader - as an alternative and/or complementary method to manual coding. METHODS: We used videos of parents (fathers, n = 36; mothers, n = 29) taken from the Avon Longitudinal Study of Parents and Children. The videos—obtained during real-life parent-infant interactions in the home—were coded both manually (using an existing coding scheme) and by FaceReader. We established a correspondence between the manual and automated coding categories - namely Positive, Neutral, Negative, and Surprise - before contingency tables were employed to examine the software’s detection rate and quantify the agreement between manual and automated coding. By employing binary logistic regression, we examined the predictive potential of FaceReader outputs in determining manually classified facial expressions. An interaction term was used to investigate the impact of gender on our models, seeking to estimate its influence on the predictive accuracy. RESULTS: We found that the automated facial detection rate was low (25.2% for fathers, 24.6% for mothers) compared to manual coding, and discuss some potential explanations for this (e.g., poor lighting and facial occlusion). Our logistic regression analyses found that Surprise and Positive expressions had strong predictive capabilities, whilst Negative expressions performed poorly. Mothers’ faces were more important for predicting Positive and Neutral expressions, whilst fathers’ faces were more important in predicting Negative and Surprise expressions. DISCUSSION: We discuss the implications of our findings in the context of future automated facial coding studies, and we emphasise the need to consider gender-specific influences in automated facial coding research.
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spelling pubmed-104252662023-08-15 Quantifying the efficacy of an automated facial coding software using videos of parents Burgess, R. Culpin, I. Costantini, I. Bould, H. Nabney, I. Pearson, R. M. Front Psychol Psychology INTRODUCTION: This work explores the use of an automated facial coding software - FaceReader - as an alternative and/or complementary method to manual coding. METHODS: We used videos of parents (fathers, n = 36; mothers, n = 29) taken from the Avon Longitudinal Study of Parents and Children. The videos—obtained during real-life parent-infant interactions in the home—were coded both manually (using an existing coding scheme) and by FaceReader. We established a correspondence between the manual and automated coding categories - namely Positive, Neutral, Negative, and Surprise - before contingency tables were employed to examine the software’s detection rate and quantify the agreement between manual and automated coding. By employing binary logistic regression, we examined the predictive potential of FaceReader outputs in determining manually classified facial expressions. An interaction term was used to investigate the impact of gender on our models, seeking to estimate its influence on the predictive accuracy. RESULTS: We found that the automated facial detection rate was low (25.2% for fathers, 24.6% for mothers) compared to manual coding, and discuss some potential explanations for this (e.g., poor lighting and facial occlusion). Our logistic regression analyses found that Surprise and Positive expressions had strong predictive capabilities, whilst Negative expressions performed poorly. Mothers’ faces were more important for predicting Positive and Neutral expressions, whilst fathers’ faces were more important in predicting Negative and Surprise expressions. DISCUSSION: We discuss the implications of our findings in the context of future automated facial coding studies, and we emphasise the need to consider gender-specific influences in automated facial coding research. Frontiers Media S.A. 2023-07-31 /pmc/articles/PMC10425266/ /pubmed/37583610 http://dx.doi.org/10.3389/fpsyg.2023.1223806 Text en Copyright © 2023 Burgess, Culpin, Costantini, Bould, Nabney and Pearson. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Burgess, R.
Culpin, I.
Costantini, I.
Bould, H.
Nabney, I.
Pearson, R. M.
Quantifying the efficacy of an automated facial coding software using videos of parents
title Quantifying the efficacy of an automated facial coding software using videos of parents
title_full Quantifying the efficacy of an automated facial coding software using videos of parents
title_fullStr Quantifying the efficacy of an automated facial coding software using videos of parents
title_full_unstemmed Quantifying the efficacy of an automated facial coding software using videos of parents
title_short Quantifying the efficacy of an automated facial coding software using videos of parents
title_sort quantifying the efficacy of an automated facial coding software using videos of parents
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10425266/
https://www.ncbi.nlm.nih.gov/pubmed/37583610
http://dx.doi.org/10.3389/fpsyg.2023.1223806
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