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Learning with Ambiguous Label Distribution for Apparent Age Estimation

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Computer Vision – ACCV 2016 (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10113))

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Abstract

Annotating age classes for humans’ facial images according to their appearance is very challenging because of dynamic person-specific ageing pattern, and thus leads to a set of unreliable apparent age labels for each image. For utilising ambiguous label annotations, an intuitive strategy is to generate a pseudo age for each image, typically the average value of manually-annotated age annotations, which is thus fed into standard supervised learning frameworks designed for chronological age estimation. Alternatively, inspired by the recent success of label distribution learning, this paper introduces a novel concept of ambiguous label distribution for apparent age estimation, which is developed under the following observations that (1) soft labelling is beneficial for alleviating the suffering of inaccurate annotations and (2) more reliable annotations should contribute more. To achieve the goal, label distributions of sparse age annotations for each image are weighted according to their reliableness and then combined to construct an ambiguous label distribution. In the light, the proposed learning framework not only inherits the advantages from conventional learning with label distribution to capture latent label correlation but also exploits annotation reliableness to improve the robustness against inconsistent age annotations. Experimental evaluation on the FG-NET age estimation benchmark verifies its effectiveness and superior performance over the state-of-the-art frameworks for apparent age estimation.

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Notes

  1. 1.

    Following the usage in Matlab, the notation [x : y : z] represents an array starting from x to z with the step of y.

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Acknowledgement

This work was funded by Academy of Finland under the Grant No. 267581 and 298700, and D2I SHOK project funded by Digile Oy and Nokia Technologies (Tampere, Finland). The authors wish to acknowledge CSC-IT Center for Science, Finland, for generous computational resources.

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Authors and Affiliations

  1. Department of Signal Processing, Tampere University of Technology, 33720, Tampere, Finland

    Ke Chen & Joni-Kristian Kämäräinen

Authors
  1. Ke Chen
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  2. Joni-Kristian Kämäräinen
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Correspondence to Ke Chen .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo , Tokyo, Japan

    Yoichi Sato

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Chen, K., Kämäräinen, JK. (2017). Learning with Ambiguous Label Distribution for Apparent Age Estimation. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-54187-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54186-0

  • Online ISBN: 978-3-319-54187-7

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