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Face recognition using discriminant locality preserving projections based on maximum margin criterion. (English) Zbl 1209.68469
Summary: We propose a new discriminant locality preserving projections based on maximum margin criterion (DLPP/MMC). DLPP/MMC seeks to maximize the difference, rather than the ratio, between the locality preserving between-class scatter and locality preserving within-class scatter. DLPP/MMC is theoretically elegant and can derive its discriminant vectors from both the range of the locality preserving between-class scatter and the range space of locality preserving within-class scatter. DLPP/MMC can also derive its discriminant vectors from the null space of locality preserving within-class scatter when the parameter of DLPP/MMC approaches $$+ \infty$$. Experiments on the ORL, Yale, FERET, and PIE face databases show the effectiveness of the proposed DLPP/MMC.

##### MSC:
 68T10 Pattern recognition, speech recognition
Yale Face; FERET
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##### References:
 [1] Fukunaga, K., Introduction to statistical pattern recognition, (1990), Academic Press Boston, USA · Zbl 0711.62052 [2] Duda, R.O.; Hart, P.E.; Stork, D.G., Pattern classification, (2000), John Wiley & Sons New York [3] Wu, J.; Zhou, Z.-H., Face recognition with one training image per person, Pattern recognition letter, 23, 14, 1711-1719, (2002) · Zbl 1007.68920 [4] Dasa, K.; Nenadic, Z., An efficient discriminant-based solution for small sample size problem, Pattern recognition, 42, 5, 857-866, (2009) · Zbl 1178.68489 [5] Raudys, S.J.; Jain, A.K., Small sample size effects in statistical pattern recognition: recommendations for practitioners, IEEE transactions on pattern analysis and machine intelligence, 13, 3, 252-264, (1991) [6] Belhumeur, V.; Hespanha, J.; Kriegman, D., Eigenfaces vs. fisherfaces: recognition using class specific linear projection, IEEE transactions on pattern analysis and machine intelligence, 19, 7, 711-720, (1997) [7] Friedman, J.H., Regularized discriminant analysis, Journal of the American statistical association, 84, 165-175, (1989) [8] Chen, L.F.; Liao, H.Y.M.; Ko, M.T.; Yu, G.J., A new LDA-based face recognition system which can solve the small sample size problem, Pattern recognition, 33, 1, 1713-1726, (2000) [9] Li, H.; Jiang, T.; Zhang, K., Efficient and robust feature extraction by maximum margin criterion, IEEE transactions on neural networks, 17, 1, 1157-1165, (2006) [10] Song, Fengxi; Zhang, Davis; Mei, Dayong, A multiple maximum scatter difference discriminant criterion for facial feature extraction, IEEE transaction on systems, man, and cybernetics-part B: cybernetics, 33, 6, 1566-1599, (2007) [11] Yan, J.; Zhang, B.; Yan, S.; Yang, Q.; Li, H.; Chen, Z.; Xi, W.; Fan, W.; Ma, W.; Cheng, Q., IMMC: incremental maximum margin criterion, Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery data mining, 725-730, (2004) [12] Zheng, W.; Zou, C.; Zhao, L., Weighted maximum margin discriminant analysis with kernels, Neurocomputing, 67, 8, 357-362, (2005) [13] Liu, Q.; Tang, X.; Lu, H.; Ma, S., Face recognition using kernel scatter-difference-based discriminant analysis, IEEE transactions neural network, 17, 4, 1081-1085, (2006) [14] Zhuang, X.S.; Dai, D.Q., Inverse Fisher discriminant criteria for small sample size problem and its application to face recognition, Pattern recognition, 38, 11, 2192-2194, (2005) [15] Zhuang, X.S.; Dai, D.Q., Improved discriminant analysis for high-dimensional data and its application to face recognition, Pattern recognition, 40, 5, 1570-1578, (2007) · Zbl 1113.68086 [16] Tenenbaum, J.B.; de Silva, V.; Langford, J.C., A global geometric framework for nonlinear dimensionality reduction, Science, 290, 2319-2323, (2000) [17] Roweis, S.T.; Saul, L.K., Nonlinear dimension reduction by locally linear embedding, Science, 290, 2323-2326, (2000) [18] Belkin, M.; Niyogi, P., Laplacian eigenmaps for dimensionality reduction and data representation, Neural computation, 15, 6, 1373-1396, (2003) · Zbl 1085.68119 [19] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Graph embedding: a general framework for dimensionality reduction, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 830-837. [20] X. He, S. Yan, Y. Hu, H. Zhang, Learning a locality preserving subspace for visual recognition, in: Proceedings of the Ninth International Conference on Computer Vision, France, October 2003, pp. 385-392. [21] He, X.; Yan, S.; Hu, Y.; Niyogi, P.; Zhang, H., Face recognition using Laplacian faces, IEEE transactions on pattern analysis and machine intelligence, 27, 3, 328-340, (2005) [22] Hu, H., Orthogonal neighborhood preserving discriminant analysis for face recognition, Pattern recognition, 41, 2045-2054, (2008) · Zbl 1132.68637 [23] Yu, W.; Teng, X.; Liu, C., Face recognition using discriminant locality preserving projections, Image vision computing, 24, 239-248, (2006) [24] Yang, L.; Gong, W.; Gu, X.; Li, W.; Liang, Y., Null space discriminant locality preserving projections for face recognition, Neurocomputing, 71, 3644-3649, (2008) [25] Liu, K.; Cheng, Y.Q.; Yang, J.Y., A generalized optimal set of discriminant vectors, Pattern recognition, 25, 7, 731-739, (1992)
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