[1]Zhang T H, Yang J, Wang H H, et al. Maximum variance projections for face recognition[J]. Optical Engineering, 2007, 46 (6): 067206.[2]Zhang T H, Li X L, Tao D C, et al. Multimodal biometrics using geometry preserving projections[J]. Pattern Recognition, 2008,41 (3): 805813.[3]Fukunaga K. Introduction to statistical pattern recognition[M]. 2nd ed. Boston :Academic Press, 1990.[4]Yan S, Xu D, Zhang B, et al. Graph embedding and extensions: A general framework for dimensionality reduction[J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(1): 4051.[5]Rosenberg S. The laplacian on a riemannian manifold[M]. Boston: Cambridge University Press, 1997.[6]Roweis S T, Saul L K . Nonlinear dimensionality reduction by locally linear embedding[J].Science, 2000,290(5500): 23232326.[7]Turk M, Pentland A. Eigenfaces for recognition[J]. Cognitive Neuroscience, 1991, 3 (1): 7186.[8]Belhumeur P, Hespanha J, Kriegman D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection[J]. IEEE Trans Pattern Anal Mach Intell, 1997, 19 (7): 711720.[9]He X F, Niyogi P. Locality preserving projections[C]//Advances in Neural Information Processing Systems. Canada: Vancouver, 2003:155160.[10]Yale University. Yale Database[EB/OL]. (20100105). http://cvc.yale.edu/projects/yalefaces/yalefaces.html.[11]Sarkar S, Phillips P J, Liu Z Y, et al. The HumanID gait challenge problem: Data sets, performance, and analysis[J]. IEEE Trans Pattern Anal Mach Intell, 2005, 27 (2): 162177.[12]Phillips P J, Sarkar S, Robledo I, et al. The gait identification challenge problem: Data sets and baseline algorithm[C]//Proc Int’l Conf Pattern Recognition. [s.l.]:[s.n.],2002: 385388. |