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    An innovative weighted 2DLDA approach to face recognition

    Access Status
    Fulltext not available
    Authors
    Lu, C.
    An, Senjian
    Liu, Wan-Quan
    Liu, X.
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Lu, C. and An, S. and Liu, W. and Liu, X. 2011. An innovative weighted 2DLDA approach to face recognition. Journal of Signal Processing Systems. 65 (1): pp. 81-87.
    Source Title
    Journal of signal processing systems
    DOI
    10.1007/s11265-010-0541-2
    ISSN
    1939-8018
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/49372
    Collection
    • Curtin Research Publications
    Abstract

    Two Dimensional Linear Discrimination Analysis (2DLDA) is an effective feature extraction approach for face recognition, which manipulates on the two dimensional image matrices directly. However, some between-class distances in the projected space are too small and this may produce a large erroneous classification rate. In this paper we propose a new 2DLDA-based approach that can overcome such drawback for the existing 2DLDA. The proposed approach redefines the between-class scatter matrix by putting a weighting function based on the between-class distances, and this will balance the between-class distances in the projected space iteratively. In order to design an effective weighting function, the between-class distances are calculated and then used to iteratively change the between-class scatter matrix, which eventually leads to an optimal projection matrix. Experimental results show that the proposed approach can improve the recognition rates on benchmark data-bases such as the ORL database, the Yale database, the YaleB database and the Feret database in comparison with other 2DLDA variants.

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