2.5D Facial Personality Prediction Based on Deep Learning
The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college stude...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5581984 |
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| _version_ | 1832550916369154048 |
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| author | Jia Xu Weijian Tian Guoyun Lv Shiya Liu Yangyu Fan |
| author_facet | Jia Xu Weijian Tian Guoyun Lv Shiya Liu Yangyu Fan |
| author_sort | Jia Xu |
| collection | DOAJ |
| description | The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images. |
| format | Article |
| id | doaj-art-8303233d71af43f29c688039dc1b607f |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-8303233d71af43f29c688039dc1b607f2025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/558198455819842.5D Facial Personality Prediction Based on Deep LearningJia Xu0Weijian Tian1Guoyun Lv2Shiya Liu3Yangyu Fan4School of Electronics and Information, Northwestern Polytechnical University, Xi’an, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an, ChinaContent Production Center of Virtual Reality, Beijing, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi’an, ChinaThe assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.http://dx.doi.org/10.1155/2021/5581984 |
| spellingShingle | Jia Xu Weijian Tian Guoyun Lv Shiya Liu Yangyu Fan 2.5D Facial Personality Prediction Based on Deep Learning Journal of Advanced Transportation |
| title | 2.5D Facial Personality Prediction Based on Deep Learning |
| title_full | 2.5D Facial Personality Prediction Based on Deep Learning |
| title_fullStr | 2.5D Facial Personality Prediction Based on Deep Learning |
| title_full_unstemmed | 2.5D Facial Personality Prediction Based on Deep Learning |
| title_short | 2.5D Facial Personality Prediction Based on Deep Learning |
| title_sort | 2 5d facial personality prediction based on deep learning |
| url | http://dx.doi.org/10.1155/2021/5581984 |
| work_keys_str_mv | AT jiaxu 25dfacialpersonalitypredictionbasedondeeplearning AT weijiantian 25dfacialpersonalitypredictionbasedondeeplearning AT guoyunlv 25dfacialpersonalitypredictionbasedondeeplearning AT shiyaliu 25dfacialpersonalitypredictionbasedondeeplearning AT yangyufan 25dfacialpersonalitypredictionbasedondeeplearning |