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These methods consider an n ₁ timesn ₂ image as a vector in R ⁿ ₁ ^timesn ₂ and the pixels of each image are considered as independent. While an image represented in the plane is intrinsically a matrix. The pixels spatially close to each other may be correlated. Even though we have n ₁ xn ₂ pixels per image, this spatial correlation suggests the real number of freedom is far less. In this paper, we introduce a regularized subspace learning model using a Laplacian penalty to constrain the coefficients to be spatially smooth. All these existing subspace learning algorithms can fit into this model and produce a spatially smooth subspace which is better for image representation than their original version. Recognition, clustering and retrieval can be then performed in the image subspace. 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Learning a Spatially Smooth Subspace for Face Recognition 论文

Learning a Spatially Smooth Subspace for Face Recognition · 相关文章