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分类概率保持鉴别分析及其在人脸识别中的应用

, PP. 77-81

Keywords: 人脸识别,特征提取,流形,分类概率,鉴别分析

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Abstract:

针对特征提取算法中存在的问题,在线性鉴别分析的基础上提出分类概率保持鉴别分析(CPPDA)并成功应用于人脸识别.CPPDA首先计算每个样本的分类概率,并利用分类概率重新定义样本的类间散布矩阵和类内散布矩阵;然后通过最大化类间散度同时最小化类内散度寻求最佳投影矩阵,使得样本的原始分布信息在低维特征空间能得到保持.在ORL、Yale及FERET人脸库上进行测试比较,结果表明文中所提方法的优越性。

References

[1]  Zhao Wenyi, Chellappa R, Phillips P J, et al. Face Recognition: A Literature Survey. ACM Computing Surveys, 2003, 35(4): 399-458
[2]  Li Wujun, Wang Chongjun, Zhang Wei, et al. A Survey of Face Recognition. Pattern Recognition and Artificial Intelligence, 2006, 19(1): 58-66 (in Chinese)(李武军,王崇骏,张 炜,等.人脸识别研究综述.模式识别与人工智能, 2006, 19(1): 58-66)
[3]  Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86
[4]  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
[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]  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
[9]  Li Junbao, Pan J S, Chu Shuchuan. Kernel Class-Wise Locality Preserving Projection. Information Science, 2008, 178(7): 1825-1835
[10]  He Xiaofei, Yan Shuicheng, Hu Yuxiao, et al. Learning a Locality Preserving Subspace for Visual Recognition // Proc of the 9th IEEE International Conference on Computer Vision. Nice, France, 2003, I: 385-392
[11]  Yan Shuicheng, Xu Dong, Zhang Benyu, et al. Graph Embedding: A General Framework for Dimensionality Reduction. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51
[12]  Beveridge J R, Bolme D S, Draper B A, et al. The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure. Machine Vision and Applications, 2005, 16(2): 128-138
[13]  Sugiyama M. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis. Journal of Machine Learning Research, 2007, 8: 1027-1061
[14]  Lai Zhihui, Zhao Cairong, Chen Yi, et al. Maximal Local Interclass Embedding with Application to Face Recognition. Machine Vision and Applications , 2011, 22(4): 619-627
[15]  Zhao Haitao, Sun Shaoyuan, Jing Zhongliang, et al. Local Structure Based Supervised Feature Extraction. Pattern Recognition, 2006, 39(8): 1546-1550
[16]  Li Bo, Wang Chao, Huang Deshuang. Supervised Feature Extraction Based on Orthogonal Discriminant Projection. Neurocomputing, 2009, 73(1/2/3): 191-196
[17]  Zhang Shangwen, Lei Yingke, Wu Yanhua, et al. Modified Orthogonal Discriminant Projection for Classification. Neurocomputing, 2011, 74(17): 3690-3694
[18]  Goldberger J, Roweis S, Hinton G, et al. Neighbourhood Components Analysis // Lawrence K. Saul, Yair Weiss, Léon Bottou, eds. Advances in Neural Information Processing Systems 17. Vancouver, Canada: MIT Press, 2004: 513-520

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