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Adaptive Variance Based Label Distribution Learning for Facial Age Estimation

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12368))

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Abstract

Estimating age from a single facial image is a classic and challenging topic in computer vision. One of its most intractable issues is label ambiguity, i.e., face images from adjacent age of the same person are often indistinguishable. Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels. Actually, most of them set a fixed value to the variance of Gaussian label distribution for all the images. However, the variance is closely related to the correlation between adjacent ages and should vary across ages and identities. To model a sample-specific variance, in this paper, we propose an adaptive variance based distribution learning (AVDL) method for facial age estimation. AVDL introduces the data-driven optimization framework, meta-learning, to achieve this. Specifically, AVDL performs a meta gradient descent step on the variable (i.e. variance) to minimize the loss on a clean unbiased validation set. By adaptively learning proper variance for each sample, our method can approximate the true age probability distribution more effectively. Extensive experiments on FG-NET and MORPH II datasets show the superiority of our proposed approach to the existing state-of-the-art methods.

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Acknowledgments

This work was supported by Key-Area Research and Development Program of Guangdong Province (No. 2019B010153001), National Natural Science Foundation of China (No. 61772527,61806200,61976210), China Postdoctoral science Foundation (No. 2019M660859), Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety (No. 2020ZDSYSKFKT04).

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xin Wen, Biying Li, Haiyun Guo, Zhiwei Liu, Ming Tang & Jinqiao Wang

  2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Xin Wen, Biying Li, Zhiwei Liu & Jinqiao Wang

  3. ObjectEye Inc., Beijing, China

    Haiyun Guo

  4. NEXWISE Co., Ltd., Guangzhou, China

    Jinqiao Wang

  5. Anyvision, Belfast, UK

    Guosheng Hu

Authors
  1. Xin Wen
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  2. Biying Li
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  3. Haiyun Guo
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  4. Zhiwei Liu
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  7. Jinqiao Wang
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Correspondence to Xin Wen .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Wen, X. et al. (2020). Adaptive Variance Based Label Distribution Learning for Facial Age Estimation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_23

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  • DOI: https://doi.org/10.1007/978-3-030-58592-1_23

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