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A Hierarchical Face Recognition Algorithm

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Advances in Machine Learning (ACML 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5828))

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Abstract

In this paper, we propose a hierarchical method for face recognition where base classifiers are defined to make predictions based on various different principles and classifications are combined into a single prediction. Some features are more relevant to particular face recognition tasks than others. The hierarchical algorithm is flexible in selecting features relevant for the face recognition task at hand. In this paper, we explore various features based on outline recognition, PCA classifiers applied to part of the face and exploitation of symmetry in faces. By combining the predictions of these features we obtain superior performance on benchmark datasets (99.25% accuracy on the ATT dataset) at reduced computation cost compared to full PCA.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Waikato, New Zealand

    Remco R. Bouckaert

Authors
  1. Remco R. Bouckaert
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Editors and Affiliations

  1. National Laboratory for Novel Software Technology, Nanjing University, 22 Hankou Road, 210093, Nanjing, China

    Zhi-Hua Zhou

  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, 567, Osaka, Ibaraki, Japan

    Takashi Washio

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© 2009 Springer-Verlag Berlin Heidelberg

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Bouckaert, R.R. (2009). A Hierarchical Face Recognition Algorithm. In: Zhou, ZH., Washio, T. (eds) Advances in Machine Learning. ACML 2009. Lecture Notes in Computer Science(), vol 5828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05224-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-05224-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05223-1

  • Online ISBN: 978-3-642-05224-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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