期刊文献+

基于双向压缩二维保局投影的人脸识别方法 被引量:1

Face Recognition Method Based on Two-Dimensional Locality Preserving Projections
下载PDF
导出
摘要 二维保局投影(2DLPP)只在图像的横向进行数据压缩,提取的特征维数较高,针对该问题,结合二维保局投影和可选的二维保局投影,提出双向压缩二维保局投影((2D)2LPP)算法。该算法从横向和纵向2个方向实施2DLPP,使图像的横向和纵向的维数都得到有效的约简。实验结果表明,(2D)2LPP在识别率和识别时间上都优于2DLPP和A2DLPP。 Aiming at the problem that Two-Dimensional Locality Preserving Projections(2DLPP) only in the horizontal direction of the image data compression and feature's dimension is high,an Alternative Two-Dimensional Locality Preserving Projections(A2DLPP) method is given,and Two-direction data compression Two-Dimensional Locality Preserving Projections((2D)2LPP) is proposed.The algorithm executes 2DLPP separately from both horizontal and vertical directions,so that the image of the horizontal and vertical dimensions is an effective reduction.Experimental results show that both the recognition rate and recognition time of(2D)2LPP are better than 2DLPP and A2DLPP.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第7期4-6,共3页 Computer Engineering
基金 国家自然科学基金资助项目(50775167) 湖北省科技攻关计划基金资助项目(2007A101c52)
关键词 保局投影 二维保局投影 特征提取 人脸识别 Locality Preserving Projections(LPP) Two-Dimensional Locality Preserving Projections(2DLPP) feature extraction face recognition
  • 相关文献

参考文献6

  • 1Turk M,Pentland A.Eigenfaces for Recognition[J].Journal of Cognitive Neurosicence,1991,3(1):71-86.
  • 2Belhumeur P N.Eigenfaces vs.Fisherface:Recognition Using Class Specific Linear Projection[J].IEEE Trans.on Pattem Anal.and Machine Intell.,1997,19(7):711-720.
  • 3He Xiaofei,Yan Shuicheng,Hu Yuxiao,et al.Face Recognition Using Laplaeianfaces[J].IEEE Trans.on PAMI,2005,27(3):328-340.
  • 4王国强,欧宗瑛,刘典婷.基于监督保持近邻投影的人脸识别[J].计算机工程,2008,34(8):4-6. 被引量:2
  • 5Hu Denwen,Feng Guiyu,Zhou Zongtan.Two-Dimensional Locality Preserving Projections(2DLPP)with Its Application to Palmprint Recogition[J].Pattern Recognition,2007,40(1):339-342.
  • 6Yu Weiwei.Two-Dimensional Discriminant Locality Preserving Projections for Face Recognition[J].Pattem Recognition,2009,30(15):1378-1383.

二级参考文献8

  • 1邓星亮,吴清.LLE算法及其应用[J].兵工自动化,2005,24(3):65-66. 被引量:8
  • 2Turk M, Pentland A. Eigenfaces for Recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.
  • 3Turk M, Pentland A. Face Recognition Using Eigenfaces[C]//Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. [S. l.]: IEEE Computer Society Press, 1991.
  • 4Belhumeur R Hespanha J, Kriegman D. Eigenfaces VS Fisherfaces: Recognition Using Class Specific Linear Projection[C]//Proc. of IEEE Transactions on Pattern Analysis and Machine Intelligence. London, UK: Springer-Verlag Press, 1997.
  • 5Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290(12): 2323 -2326.
  • 6Belkin M, Niyogi E Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J]. Neural Computation, 2003, 15(6): 1737-1396.
  • 7Tenenbaum J B, De Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science, 2000, 290(12): 2319-2323.
  • 8Pang Yanwei, Zhang Lei, Liu Zhengkai. Neighborhood Preserving Projections: A Novel Linear Dimension Reduction Method[C]//Proc of International Conference on Intelligent Computing. [S. l.]: Springer-Verlag Press, 2005.

共引文献1

同被引文献3

引证文献1

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部