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Face recognition with adaptive local hyperplane algorithm

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Abstract

The paper introduces a novel adaptive local hyperplane (ALH) classifier and it shows its superior performance in the face recognition tasks. Four different feature extraction methods (2DPCA, (2D)2PCA, 2DLDA and (2D)2LDA) have been used in combination with five classifiers (K-nearest neighbor (KNN), support vector machine (SVM), nearest feature line (NFL), nearest neighbor line (NNL) and ALH). All the classifiers and feature extraction methods have been applied to the renown benchmarking face databases—the Cambridge ORL database and the Yale database and the ALH classifier with a LDA based extractor outperforms all the other methods on them. The ALH algorithm on these two databases is very promising but more study on larger databases need yet to be done to show all the advantages of the proposed algorithm.

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Authors and Affiliations

  1. Faculty of Engineering, The University of Auckland, Auckland, New Zealand

    Tao Yang

  2. Virginia Commonwealth University, Richmond, VA, USA

    Vojislav Kecman

Authors
  1. Tao Yang
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  2. Vojislav Kecman
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Correspondence to Tao Yang.

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Yang, T., Kecman, V. Face recognition with adaptive local hyperplane algorithm. Pattern Anal Applic 13, 79–83 (2010). https://doi.org/10.1007/s10044-008-0138-6

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  • DOI: https://doi.org/10.1007/s10044-008-0138-6

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