期刊文献+

核邻域保持判别嵌入在人脸识别中的应用

Application of Kernel Neighborhood Preserving Discriminant Embedding in Face Recognition
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摘要 为更有效地进行数据降维,将核映射思想引入到邻域保持判别嵌入中,提出一种核邻域保持判别嵌入的流形学习算法。以类内相似度矩阵与类间散度矩阵之差作为鉴别准则,使类间散度矩阵不受满秩的约束,从而解决人脸数据的非线性和小样本问题。在ORL和Yale人脸库上的实验结果表明,该算法具有较好的人脸识别性能。 In order to have a data reduction more effectively,this paper proposes a new manifold learning algorithm named Kernel Neighborhood Preserving Discriminant Embedding(KNPDE) which puts kernel mapping into the Neighborhood Preserving Discriminant Embedding(NPDE).The algorithm adopts the difference of between within-class similarity matrix and between-class scatter matrix as the discriminant criterion.So it can avoid receiving the restraint of full rank of within-class scatter matrix.The algorithm is applied to the face recognition and solves the problem of nonlinear and small sample for face data.The experiment results on the ORL and Yale face database show that this algorithm has a good recognition performance.
作者 王燕 白万荣
出处 《计算机工程》 CAS CSCD 2012年第1期163-164,167,共3页 Computer Engineering
基金 甘肃省自然科学基金资助项目(1014RJZA009)
关键词 核方法 邻域保持判别嵌入 数据降维 流形学习 人脸识别 kernel method Neighborhood Preserving Discriminant Embedding(NPDE) data dimension reduction manifold learning face recognition
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参考文献6

  • 1Turk M, Pentland A. Eigenface for Recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 72-86.
  • 2Belhumeur P, Hespanha J, Kriegman D. Eigenfaces vs. Fisherfaces Recognition Using Class Specific Linear Projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
  • 3Roweis S L, Saul L. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290(5500): 2323- 2326.
  • 4He Xiaofei, Cai Deng, Yan Shuicheng, et al. Neighborhood Preserving Embedding[C]//Proc. of International Conference on Computer Vision. Beijing, China: IEEE Press, 2005:1208-1213.
  • 5王国强,石念峰,郭玉珂.基于正交判别邻域保持投影的人脸识别[J].仪器仪表学报,2009,30(8):1734-1738. 被引量:13
  • 6王超,王士同.最大间距准则与局部保持结合的特征提取方法[J].计算机工程,2009,35(14):209-211. 被引量:4

二级参考文献15

  • 1李勇智,杨静宇,郑宇杰,夏永泉.基于最大间距准则(MMC)新的有效特征提取方法[J].系统仿真学报,2007,19(5):1061-1066. 被引量:5
  • 2祝磊,朱善安.人脸识别的一种新的特征提取方法[J].光电工程,2007,34(6):122-125. 被引量:7
  • 3TENENBAUM J B, DE SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500):2319-2323.
  • 4ROWELS S L, SAUL L. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326.
  • 5BELKIN M, NIYOGI E Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation, 2003,15(6): 1373-1396.
  • 6ZHANG Z Y, ZHA H Y. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment[J]. SIAM Journal of Scientific Computing, 2004, 26(1): 313-338.
  • 7HE X F, YAN S, HU Y, et al. Face recognition using laplacianfaces[J]. IEEE Trans. on Pattern Analysis and Ma- chine Intelligence, 2005,27(3):328-340.
  • 8TURK M, PENTLAND A. Eigenface for recognition[J]. Journal of Cognitive Neuoscience, 1991,3(1):71-86.
  • 9BELHUMEUR P, HESPANHA J, KRIEGMAN D. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):711-720.
  • 10PANG Y W, ZHANG L, LIU Z K, et al. Neighborhood preserving projections(NPP): A novel linear dimension reduction method[C]. ICIC 2005,Part I, Lecture Notes in Computer Science, 2005,3644:117 - 125.

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