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Asian Conference on Computer Vision

ACCV 2014: Computer Vision – ACCV 2014 pp 115–128Cite as

Age Estimation Based on Complexity-Aware Features

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

Abstract

The research related to age estimation using face images has become increasingly important. We propose an age estimator using two kinds of local features, the gradient features which well describe the local characteristic, and the Gabor wavelets which reflect the multi-scale directional information. The RealAdaBoost algorithm with a complexity penalty term in the feature selection module is applied to choose meaningful regions from human face for feature extraction, while balancing the discriminative capability and the computation cost at the same time. Furthermore, the hierarchical classifier, which is composed of an age group classification (e.g., 15–39 years old, 40–59 years old etc.) and a detailed age estimation (e.g. 19, 53 years old, etc.) are utilized to get the final age. Experimental results show that the proposed approach outperforms the methods using single feature on PAL and FG-NET database. It also achieves competitive accuracy with the state-of-the-art algorithms.

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Acknowledgement

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under the Grant RGP36726.

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Authors and Affiliations

  1. Vision and Media Lab School of Computing Science, Simon Fraser University, 8888 University Drive, Vancouver, BC, Canada

    Haoyu Ren & Ze-Nian Li

Authors
  1. Haoyu Ren
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  2. Ze-Nian Li
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Corresponding author

Correspondence to Haoyu Ren .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Ren, H., Li, ZN. (2015). Age Estimation Based on Complexity-Aware Features. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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