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  • Spectral Regression dimension reduction for multiple features facial image retrieval

    Author
    Zhang, Bailing
    Gao, Yongsheng
    Year published
    2012
    Metadata
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    Abstract
    Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the ...
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    Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.
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    Journal Title
    International Journal of Biometrics
    Volume
    4
    Issue
    1
    DOI
    https://doi.org/10.1504/IJBM.2012.044296
    Subject
    Computer Vision
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/47170
    Collection
    • Journal articles

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