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Identifying Big Five personality traits based on facial behavior analysis

The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the tec...

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Autores principales: Cai, Lei, Liu, Xiaoqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9533697/
https://www.ncbi.nlm.nih.gov/pubmed/36211657
http://dx.doi.org/10.3389/fpubh.2022.1001828
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author Cai, Lei
Liu, Xiaoqian
author_facet Cai, Lei
Liu, Xiaoqian
author_sort Cai, Lei
collection PubMed
description The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the technology of automatic personality identification based on facial analysis has become the most advanced research direction in large-scale personality identification technology. This study proposes a method to automatically identify the Big Five personality traits by analyzing the facial movement in ordinary videos. In this study, we collected a total of 82 sample data. First, through the correlation analysis between facial features and personality scores, we found that the points from the right jawline to the chin contour showed a significant negative correlation with agreeableness. Simultaneously, we found that the movements of the left cheek's outer contour points in the high openness group were significantly higher than those in the low openness group. This study used a variety of machine learning algorithms to build the identification model on 70 key points of the face. Among them, the CatBoost regression algorithm has the best performance in the five dimensions, and the correlation coefficients between the model prediction results and the scale evaluation results are about medium correlation (0.37–0.42). Simultaneously, we executed the Split-Half reliability test, and the results showed that the reliability of the experimental method reached a high-reliability standard (0.75–0.96). The experimental results further verify the feasibility and effectiveness of the automatic assessment method of Big Five personality traits based on individual facial video analysis.
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spelling pubmed-95336972022-10-06 Identifying Big Five personality traits based on facial behavior analysis Cai, Lei Liu, Xiaoqian Front Public Health Public Health The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the technology of automatic personality identification based on facial analysis has become the most advanced research direction in large-scale personality identification technology. This study proposes a method to automatically identify the Big Five personality traits by analyzing the facial movement in ordinary videos. In this study, we collected a total of 82 sample data. First, through the correlation analysis between facial features and personality scores, we found that the points from the right jawline to the chin contour showed a significant negative correlation with agreeableness. Simultaneously, we found that the movements of the left cheek's outer contour points in the high openness group were significantly higher than those in the low openness group. This study used a variety of machine learning algorithms to build the identification model on 70 key points of the face. Among them, the CatBoost regression algorithm has the best performance in the five dimensions, and the correlation coefficients between the model prediction results and the scale evaluation results are about medium correlation (0.37–0.42). Simultaneously, we executed the Split-Half reliability test, and the results showed that the reliability of the experimental method reached a high-reliability standard (0.75–0.96). The experimental results further verify the feasibility and effectiveness of the automatic assessment method of Big Five personality traits based on individual facial video analysis. Frontiers Media S.A. 2022-09-09 /pmc/articles/PMC9533697/ /pubmed/36211657 http://dx.doi.org/10.3389/fpubh.2022.1001828 Text en Copyright © 2022 Cai and Liu. 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 Public Health
Cai, Lei
Liu, Xiaoqian
Identifying Big Five personality traits based on facial behavior analysis
title Identifying Big Five personality traits based on facial behavior analysis
title_full Identifying Big Five personality traits based on facial behavior analysis
title_fullStr Identifying Big Five personality traits based on facial behavior analysis
title_full_unstemmed Identifying Big Five personality traits based on facial behavior analysis
title_short Identifying Big Five personality traits based on facial behavior analysis
title_sort identifying big five personality traits based on facial behavior analysis
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9533697/
https://www.ncbi.nlm.nih.gov/pubmed/36211657
http://dx.doi.org/10.3389/fpubh.2022.1001828
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