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Retracted: Human Age Estimation by Metric Learning for Regression Problems

  • Conference paper
Computer Analysis of Images and Patterns (CAIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5702))

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Abstract

The estimation of human age from face images is an interesting problem in computer vision. We proposed a general distance metric learning scheme for regression problems, which utilizes not only data themselves, but also their corresponding labels to strengthen the credibility of distances. This metric could be learned by solving an optimization problem. Furthermore, the test data could be projected to this metric by a simple linear transformation and it is feasible to be combined with manifold learning algorithms to improve their performance. Experiments are conducted on the public FG-NET database by Gaussian process regression in the learned metric to validate our framework, which shows that the performance is improved over traditional methods.

An Erratum can be found at http://dx.doi.org/10.1007/978-3-642-03767-2_152

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Author information

Authors and Affiliations

  1. Max Planck Institute for Mathematics in the Sciences, 27 Inselstrasse, Leipzig, Germany

    Yangjing Long

Authors
  1. Yangjing Long
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Münster, Einsteinstrasse 62, 48149, Münster, Germany

    Xiaoyi Jiang

  2. Institute of Mathematics and Computing Science, University of Groningen, Nijenborgh 9, 9747, Groningen, AG, The Netherlands

    Nicolai Petkov

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© 2009 Springer-Verlag Berlin Heidelberg

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Cite this paper

Long, Y. (2009). Retracted: Human Age Estimation by Metric Learning for Regression Problems. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-03767-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03766-5

  • Online ISBN: 978-3-642-03767-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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