Age Estimation Using Local Binary Pattern Kernel Density Estimate

  • Juha Ylioinas
  • Abdenour Hadid
  • Xiaopeng Hong
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

We propose a novel kernel method for constructing local binary pattern statistics for facial representation in human age estimation. For age estimation, we make use of the de facto support vector regression technique. The main contributions of our work include (i) evaluation of a pose correction method based on simple image flipping and (ii) a comparison of two local binary pattern based facial representations, namely a spatially enhanced histogram and a novel kernel density estimate. Our single- and cross-database experiments indicate that the kernel density estimate based representation yields better estimation accuracy than the corresponding histogram one, which we regard as a very interesting finding. In overall, the constructed age estimation system provides comparable performance against the state-of-the-art methods. We are using a well-defined evaluation protocol allowing a fair comparison of our results.

Keywords

Face Recognition Face Image Local Binary Pattern Kernel Density Estimate Facial Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juha Ylioinas
    • 1
  • Abdenour Hadid
    • 1
  • Xiaopeng Hong
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Center for Machine Vision ResearchUniversity of OuluFinland

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