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A Multifeature Learning and Fusion Network for Facial Age Estimation

Abstract : 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.
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https://hal.utc.fr/hal-03515051
Contributor : Imad Rida Connect in order to contact the contributor
Submitted on : Thursday, January 6, 2022 - 3:16:42 PM
Last modification on : Monday, January 24, 2022 - 7:22:02 PM
Long-term archiving on: : Thursday, April 7, 2022 - 7:36:45 PM

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Yulan Deng, Shaohua Teng, Lunke Fei, Wei Zhang, Imad Rida. A Multifeature Learning and Fusion Network for Facial Age Estimation. Sensors, MDPI, 2021, 21 (13), pp.4597. ⟨10.3390/s21134597⟩. ⟨hal-03515051⟩

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