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,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4597 |
| _version_ | 1797527843875323904 |
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| author | Yulan Deng Shaohua Teng Lunke Fei Wei Zhang Imad Rida |
| author_facet | Yulan Deng Shaohua Teng Lunke Fei Wei Zhang Imad Rida |
| author_sort | Yulan Deng |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T09:49:35Z |
| format | Article |
| id | doaj.art-2139848f9d774390963faa88d1e709bc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:49:35Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2139848f9d774390963faa88d1e709bc2023-11-22T02:51:51ZengMDPI AGSensors1424-82202021-07-012113459710.3390/s21134597A Multifeature Learning and Fusion Network for Facial Age EstimationYulan Deng0Shaohua Teng1Lunke Fei2Wei Zhang3Imad Rida4School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, ChinaCentre de Recherches de Royallieu, Université de Technologie de Compiègne, 76800 Compiègne, FranceAge 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.https://www.mdpi.com/1424-8220/21/13/4597age estimationmultifeature learningfeature fusionregression-ranking estimator |
| spellingShingle | Yulan Deng Shaohua Teng Lunke Fei Wei Zhang Imad Rida A Multifeature Learning and Fusion Network for Facial Age Estimation Sensors age estimation multifeature learning feature fusion regression-ranking estimator |
| 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 | age estimation multifeature learning feature fusion regression-ranking estimator |
| url | https://www.mdpi.com/1424-8220/21/13/4597 |
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