Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embedding model, called Semi-supervised Graph Embedding (SGE). This algorithm builds an adjacency graph which can best respect the geometry structure inferred from the must-link pairwise constraints, which specify a pair of instances belong to the same class. The projections are obtained by preserving such a graph structure. Using the notion of graph Laplacian, SGE has a closed solution of an eigen-problem of some specific Laplacian matrix and therefore it is quite efficient. Experimental results on Yale standard face database demonstrate the effectiveness of our proposed algorithm.
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