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- W56688174 abstract "This paper focuses on enhancing Sparse Representation based Classifier (SRC) in single-sample face recognition tasks under varying illumination conditions. The major contribution is two-fold: firstly, we present an interesting observation based on Lambertian reflectance model: the identity information will be canceled out by the pair-wise difference images from the same subject in logarithmic domain, and only the subject-independent illumination variation retains. Secondly, inspired from this observation, we propose to “borrow” illumination variations from any generic subject by constructing an illumination variation dictionary composed of pair-wise difference images of generic subjects in logarithmic domain to cover the possible illumination variations between test and gallery samples. Experimental results on Extended Yale B and FERET face databases demonstrate the superiority of our method." @default.
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- W56688174 date "2013-01-01" @default.
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- W56688174 title "Illumination Variation Dictionary Designing for Single-Sample Face Recognition via Sparse Representation" @default.
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- W56688174 doi "https://doi.org/10.1007/978-3-642-35728-2_42" @default.
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