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Linear Discriminant Analysis (LDA) is a powerful technology for supervised dimensionality reduction, however, it only captures the extrinsic (or global) structure in the data and fails to discover the intrinsic structure of the data manifold. In this paper, we develop a new linear supervised dimensionality reduction method, called Graph Regularized Discriminant Analysis(GRDA), which respects both...
In this paper, we propose an example-based facial sketch hallucination approach. Given a face image, its sketch image will be automatically hallucinated by learning from a training set, which includes a lot of face images and their corresponding sketch images. Our algorithm involves three stages. In the first stage which is called ??feature extracting??, we create the feature pyramid for each face...
Regularization plays a vital role in ill-posed problems. A properly chosen regularization can direct the solution toward a better quality outcome. An emerging powerful regularization is one that leans on image examples. In this paper, we propose a novel scheme for face hallucination. We target specially the quality of highly zoomed outputs. Our work bases on the pyramid framework and assigns several...
In this paper, we propose a novel face hallucination approach based on the pyramid regression strategy'. A pyramid framework is built which divides the whole face hallucination process into several levels. The output of last level is the input of next one. For the face hallucination at each level, the searching strategy is mixed: globally best-matching and locally best-matching. The former is got...
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