Face Recognition Based on Kernel Discriminative Common Vectors

Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.[2]Yu H, Yang J. A Direct LDA algorithm for high-dimensional data with application to face recognition[J].Pattern Recognition.2001, 34(10):2067-2070[3]Chen L F, Liao H YM, Ko M T, Lin JC, Yu G J. A new LDA-based face recognition system which can solve the small sample size problem[J].Pattern Recognition.2000, 33(10):1713-1726[4]Huang R, Liu Q, Lu H, Ma S. Solving the small size problem of LDA[J].Proc. 16th Intl Conf. Pattern Recognition, Quebec City, Que., Canada.2002, 3(8):29-32[5]Cevikalp H, Neamtu M, Wilkes M, Barkana A. Discriminativecommon vectors for face recognition[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2005, 27(1):4-13[6]Glmezoglu M B, Dzhafarov V, Barkana A. The common vector approach and its relation to principal component analysis[J].IEEE Trans. Speech and Audio Processing.2001, 9(6):655-662[7]Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. England: Cambridge Univ. Press, 2004, Part 2.[8]程云鹏. 矩阵论[M]. 西安: 西北工业大学出版社, 2001, 第4章.[9]Foley D H, Sammon J W. An optimal set of discriminant vectors[J].IEEE Trans. on Comput.1975, 24(3):281-289[10]Yang J, Jin Z, Yang JY. Essence of kernel Fisher discriminant: KPCA plus LDA[J].Pattern Recognition.2004, 37(10):2097-2100