Fast Complete Discriminant Locality Preserving Projections for Face Recognition
LU Gui-Fu1,2, WANG Yong 2, JIN Zhong1
1.School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094 2.School of Computer and Information, Anhui Polytechnic University, Wuhu 241000
Abstract：Fast complete discriminant locality preserving projections (FCDLPP) is proposed. There is only one step of economic QR factorization for FCDLPP algorithm to obtain the optimal discriminant vectors in the null space of locality preserving within-class scatter. Then, one step of eigen-decomposition is used to obtain the optimal discriminant vectors in the principal space of the locality preserving within-class scatter. Besides, FCDLPP fuses the regular discriminant features in the principal space and irregular discriminant features in the null space. Theoretical analyses and experimental results show that the proposed FCDLPP outperforms complete discriminant locality preserving projections (CDLPP) on computational speed and recognition rates.
卢桂馥，王勇，金忠. 快速的完备鉴别保局投影人脸识别算法[J]. 模式识别与人工智能, 2011, 24(6): 804-809.
LU Gui-Fu, WANG Yong , JIN Zhong. Fast Complete Discriminant Locality Preserving Projections for Face Recognition. , 2011, 24(6): 804-809.
[1] Duda R O, Hart P E, Stork D G. Pattern Classification. 2nd Edition. New York,USA: John Wiley Sons, 2000 [2] 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 [3] Chen Lifen, Liao Hongyuan, Ko M T, et al. A New LDA-Based Face Recognition System Which Can Solve the Small Sample Size Problem. Pattern Recognition, 2000, 33(10): 1713-1726 [4] Li Haifeng, Jiang Tao, Zhang Keshu. Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans on Neural Networks, 2006, 7(1): 157-165 [5] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326. [6] He Xiaofei, Yan Shuicheng, Hu Yuxiao, et al. Face Recognition Using Laplacianfaces. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340 [7]Yu Weiwei, Teng Xiaolong, Liu Chongqing. Face Recognition Using Discriminant Locality Preserving Projections. Image and Vision Computing, 2006, 24(3): 239-248. [8] Yang Liping, Gong Weiguo, Gu Xiaohua, et al. Bagging Null Space Locality Preserving Discriminant Classifiers for Face Recognition. Pattern Recognition, 2009, 42(9): 1853-1858 [9] Yang Liping, Gong Weiguo, Gu Xiaohua, et al. Complete Discriminant Locality Preserving Projections for Face Recognition. Journal of Software, 2010, 21(6): 1277-1286 (in Chinese) (杨利平,龚卫国,辜小花,等.完备鉴别保局投影人脸识别算法.软件学报, 2010, 21(6): 1277-1286 ) [10] Golub G H, van Loan C F. Matrix Computations. 3rd Edition. Baltimore, USA: The Johns Hopkins University Press, 1996 [11] Yang Jian, Frangi A F, Yang Jingyu, et al, KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(2): 230-244
2011, Vol. 24 
