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Traditional face superresolution methods treat face images as 1D vectors and apply PCA on the set of these 1D vectors to learn the face subspace. Zhang et al [7] proposed Two-directional two-dimensional PCA (2D)2-PCA for efficient face representation and recognition where images are treated as matrices instead of vectors. In this paper, we present a two-step algorithm for face superresolution. In...
Generally face images may be visualized as points drawn on a low-dimensional manifold embedded in high-dimensional ambient space. Many dimensionality reduction techniques have been used to learn this manifold. Orthogonal locality preserving projection (OLPP) is one among them which aims to discover the local structure of the manifold and produces orthogonal basis functions. In this paper, we present...
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