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Age Estimation of Asian Face Based on Feature Map of Texture Difference Model

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Recent Developments in Intelligent Systems and Interactive Applications (IISA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 541))

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Abstract

Combining with Support Vector Regression (SVR), this paper proposes a feature map of texture difference (FMTD) model for Asian face age estimation. The FMTD model is based on the standard bio-inspired feature model and can learn the feature information of the important face organs area as well as the wrinkles area. The learnt feature is strengthened by image processing, including image difference, down scaling, dividing and max-pooling. The resulting feature is sensitive to age estimation. Experimental results on the Asian face dataset and two public datasets prove that the proposed method reduces the mean absolute error (MAE) of age estimation comparing with other current methods and improves the degree of accuracy, which results in effective age estimation for Asian face.

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Acknowledgments

This work is supported by Application Technology Research & Demonstration Promotion Project of Hainan Province of China under grant ZDXM2015103 and JDJS2013006.

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Authors and Affiliations

  1. Haikou Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Haikou, China

    Jie Yang, Peng Liu, Yuxin Jiang & Songbin Li

Authors
  1. Jie Yang
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  2. Peng Liu
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  3. Yuxin Jiang
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  4. Songbin Li
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Corresponding author

Correspondence to Songbin Li .

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Editors and Affiliations

  1. Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

  2. SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Yang, J., Liu, P., Jiang, Y., Li, S. (2017). Age Estimation of Asian Face Based on Feature Map of Texture Difference Model. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-49568-2_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49567-5

  • Online ISBN: 978-3-319-49568-2

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