Mixture-of-Laplacian Faces and Its Application to Face Recognition
The locality preserving projection (LPP), known as Laplacianfaces, was recently proposed as a transformation technique of mapping which optimally preserves the neighborhood structure of the dataset. In this paper, an efficient method for face recognition called mixture-of-Laplacianfaces (or LPP mixture model) is proposed, which obtains several sets of Laplacianfaces through Expectation-Maximization (EM) learning of Gaussian Mixture Models (GMM). Experiments carried out by using this on ORL, FERET and COIL-20 indicate superior performance as compared with method based on Laplacianfaces and other contemporary subspace methods.
KeywordsTraining Sample Face Recognition Gaussian Mixture Model Training Image Recognition Accuracy
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