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Advances in Face Image Analysis:: Theory and applications - download pdf or read online

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ISBN-10: 1681081113

ISBN-13: 9781681081113

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The Complete Kernel Fisher Discriminant method [36]). It should be noticed that the Marginal Fisher Analysis (MFA) method [20] and the LDE method are essentially the same. In [37], the authors proposed another variant of LDE by using a difference criterion instead of a quotient. In the sequel, capital bold letters denote matrices and small bold letters denote vectors. Let l(xi ) be the class label of xi. For each data sample xi, two subsets, Nw(xi ) and Nb(xi ) are generated. One simple possible way to compute these two sets of neighbors associated with the local sample is the use of two nearest neighbor graphs: one nearest neighbor graph for homogeneous samples (parameterized by K1) and one nearest neighbor graph for heterogeneous samples (parameterized by K2).

1406. 2006; 28(12): 2037-2041. 2012; 45(1): 80-91. Rep; 2014. Regianini L.. , . Manual annotations of facial fiducial points on the Cohn Kanade database.. Rep. , Masson M.. 2002; 17(9): 657-673. , Erhan D.. , . Advances in Neural Information Processing Systems. 2013. -2561. Sparsity Preserving Projection Based Constrained Graph Embedding and Its Application to Face Recognition Libo Weng1, 2, Zhong Jin1, *, Fadi Dornaika2, 3 1 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China 2 Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, San Sebastian, Spain 3 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain Abstract In this chapter, a novel semi-supervised dimensionality reduction algorithm is proposed, namely Sparsity Preserving Projection based Constrained Graph Embedding (SPP-CGE).

Html CONFLICT OF INTEREST The authors confirm that this chapter contents have no conflict of interest. ACKNOWLEDGEMENTS This work is partially supported by National Natural Science Foundation of China under Grant Nos. 2014CB349303. John Wiley & Sons; 2012. J.. , . Modern multidimensional scaling: Theory and applications. Springer Science & [2] Business Media; 2005. K.. , Science. 2000; [3] 290(5500): 2323-2326. C.. 2000; 290(5500): 2319-2323. , Niyogi P.. 2003; 15(6): 1373-1396. 2003; 16: 234-241.

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