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A Multifeature Learning and Fusion Network for Facial Age Estimation
Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271811/ https://www.ncbi.nlm.nih.gov/pubmed/34283133 http://dx.doi.org/10.3390/s21134597 |
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author | Deng, Yulan Teng, Shaohua Fei, Lunke Zhang, Wei Rida, Imad |
author_facet | Deng, Yulan Teng, Shaohua Fei, Lunke Zhang, Wei Rida, Imad |
author_sort | Deng, Yulan |
collection | PubMed |
description | Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation. |
format | Online Article Text |
id | pubmed-8271811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82718112021-07-11 A Multifeature Learning and Fusion Network for Facial Age Estimation Deng, Yulan Teng, Shaohua Fei, Lunke Zhang, Wei Rida, Imad Sensors (Basel) Article Age estimation from face images has attracted much attention due to its favorable and many real-world applications such as video surveillance and social networking. However, most existing studies usually learn a single kind of age feature and ignore other appearance features such as gender and race, which have a great influence on the age pattern. In this paper, we proposed a compact multifeature learning and fusion method for age estimation. Specifically, we first used three subnetworks to learn gender, race, and age information. Then, we fused these complementary features to further form more robust features for age estimation. Finally, we engineered a regression-ranking age-feature estimator to convert the fusion features into the exact age numbers. Experimental results on three benchmark databases demonstrated the effectiveness and efficiency of the proposed method on facial age estimation in comparison to previous state-of-the-art methods. Moreover, compared with previous state-of-the-art methods, our model was more compact with only a 20 MB memory overhead and is suitable for deployment on mobile or embedded devices for age estimation. MDPI 2021-07-05 /pmc/articles/PMC8271811/ /pubmed/34283133 http://dx.doi.org/10.3390/s21134597 Text en © 2021 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 Deng, Yulan Teng, Shaohua Fei, Lunke Zhang, Wei Rida, Imad A Multifeature Learning and Fusion Network for Facial Age Estimation |
title | A Multifeature Learning and Fusion Network for Facial Age Estimation |
title_full | A Multifeature Learning and Fusion Network for Facial Age Estimation |
title_fullStr | A Multifeature Learning and Fusion Network for Facial Age Estimation |
title_full_unstemmed | A Multifeature Learning and Fusion Network for Facial Age Estimation |
title_short | A Multifeature Learning and Fusion Network for Facial Age Estimation |
title_sort | multifeature learning and fusion network for facial age estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271811/ https://www.ncbi.nlm.nih.gov/pubmed/34283133 http://dx.doi.org/10.3390/s21134597 |
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