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Feature Extraction Method of Maximum Scatter Difference Based on Preserving Projection |
WANG Jian-Guo1,2, YANG Wan-Kou1, YANG Jing-Yu1 |
1.School of Computer Science and Technology, Nanjing University of Science and Technology,Nanjing 210094 2.Network and Education Center, Tangshan College, Tangshan 063000 |
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Abstract Firstly, the unsupervised discriminant projection (UDP) criterion is modified. Then, the feature extraction method of the maximum scatter difference based on preserving projection is proposed on the basis of the modified discriminant criterion. The proposed method adopts the difference of both nonlocal scatter and local scatter as discriminant criterion. Thus, the singular problem of local scatter caused by small sample size problem in UDP linear discriminant analysis is avoided. Finally, experimental results on Yale and FERET face databases demonstrate the effectiveness of the proposed method.
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Received: 12 November 2007
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[1] Chen Lifen, Liao H Y M, 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 [2] Yang Jian, Yang Jingyu. Why Can LDA Be Performed in PCA Transformed Space? Pattern Recognition, 2000, 36(2): 563-566 [3] Zhao W, Krishnaswamy A, Chellappa R, et al. Discriminant Analysis of Principal Components for Face Recognition // Proc of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Nara, Japan, 1998: 336-341 [4] Yang Jian, Zhang D, Frangi A F, et al. Two-Dimensional PCA: A New Approach to Face Representation and Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137 [5] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 2000, 290(5500): 2323-2326 [6] Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 2000, 290(5500): 2319-2323 [7] Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 2003, 15(6): 1373-1396 [8] Belkin M, Niyogi P. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering // Dietterich T G, Becker S, Ghahramani Z, eds. Advances in Neural Information Processing System. Vancouver, Canada, 2001, 14: 585-591 [9] Bengio Y, Paiement J F, Vincent P, et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. Neural Computation, 2004, 16(10): 2197-2219 [10] 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 [11] Yang Jian, Zhang D, Yang Jingyu. “Non-Locality” Preserving Projection and Its Application to Palmprint Recognition // Proc of the 9th International Conference on Control, Automation, Robotics and Vision. Singapore, Singapore, 2006: 1-4 [12] Yang Jian, Zhang D, Yang Jingyu, et al. Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(4): 650- 664 [13] Bian Zhaoqi, Zhang Xuegong. Pattern Recognition. 2nd Edition. Beijing, China: Tsinghua University Press, 2000 (in Chinese) (边肇祺,张学工.模式识别. 第2版.北京:清华大学出版社, 2000) |
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