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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Noushath, S., Rao, A., & Kumar, G. H. (2007). Mixture-of-Laplacian faces and its application to face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4815 LNCS, pp. 568–575). Springer Verlag. https://doi.org/10.1007/978-3-540-77046-6_70
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