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    A novel weighted fuzzy LDA for face recognition using the genetic algorithm

    Access Status
    Fulltext not available
    Authors
    Xue, Mingliang
    Liu, Wan-Quan
    Liu, X.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Xue, M. and Liu, W. and Liu, X. 2013. A novel weighted fuzzy LDA for face recognition using the genetic algorithm. Neural Computing and Applications. 22 (7-8): pp. 1531-1541.
    Source Title
    Neural Computing and Applications
    DOI
    10.1007/s00521-012-0962-x
    ISSN
    0941-0643
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/17060
    Collection
    • Curtin Research Publications
    Abstract

    Fuzzy linear discriminate analysis (FLDA), the principle of which is the remedy of class means via fuzzy optimization, is proven to be an effective feature extraction approach for face recognition. However, some of the between-class distances in the projected space after FLDA may be too small, which can render some classes inseparable. In this paper we propose a weighted FLDA approach that aims to increase the smallest of the between-class distances. This is accomplished by introducing some weighting coefficients to the between-class distances in FLDA. Since the optimal selection of these weighting coefficients is not tractable via standard optimization techniques, the genetic algorithm is adopted as an alternative solution in this paper. The experimental results on some benchmark data sets reveal that the proposed weighted fuzzy LDA can improve the worst recognition rate effectively and also exceed LDA and FLDA’s average performance index.

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