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    An innovative face image enhancement based on principle component analysis

    Access Status
    Fulltext not available
    Authors
    Xu, Xiang
    Liu, Wan-Quan
    Venkatesh, Svetha
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Xu, X. and Liu, W. and Venkatesh, S. 2012. An innovative face image enhancement based on principle component analysis. International Journal of Machine Learning and Cybernetics. 3 (4): pp. 259-267.
    Source Title
    International Journal of Machine Learning and Cybernetics
    DOI
    10.1007/s13042-011-0060-x
    ISSN
    1868-8071
    School
    Department of Computing
    Remarks

    www.springerlink.com

    URI
    http://hdl.handle.net/20.500.11937/40390
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we propose an innovative face hallucination approach based on principle component analysis (PCA) and residue technique. First, the relationship of projection coefficients between high-resolution and low-resolution images using PCA is investigated. Then based on this analysis, a high resolution global face image is constructed from a low resolution one. Next a high-resolution residue is derived based on the similarity between the projections on high and low resolution residue training sets. Finally by combining the global face and residue in high resolution, a high resolution face image is generated. Also the recursive and two-stage methods are proposed, which improve the results of face image enhancement. Extensive experiments validate the proposed approaches.

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