2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

张量局部Fisher判别分析的人脸识别

郑建炜 王万良 姚晓敏 石海燕

郑建炜, 王万良, 姚晓敏, 石海燕. 张量局部Fisher判别分析的人脸识别. 自动化学报, 2012, 38(9): 1485-1495. doi: 10.3724/SP.J.1004.2012.01485
引用本文: 郑建炜, 王万良, 姚晓敏, 石海燕. 张量局部Fisher判别分析的人脸识别. 自动化学报, 2012, 38(9): 1485-1495. doi: 10.3724/SP.J.1004.2012.01485
ZHENG Jian-Wei, WANG Wan-Liang, YAO Xiao-Ming, SHI Hai-Yan. Face Recognition Using Tensor Local Fisher Discriminant Analysis. ACTA AUTOMATICA SINICA, 2012, 38(9): 1485-1495. doi: 10.3724/SP.J.1004.2012.01485
Citation: ZHENG Jian-Wei, WANG Wan-Liang, YAO Xiao-Ming, SHI Hai-Yan. Face Recognition Using Tensor Local Fisher Discriminant Analysis. ACTA AUTOMATICA SINICA, 2012, 38(9): 1485-1495. doi: 10.3724/SP.J.1004.2012.01485

张量局部Fisher判别分析的人脸识别

doi: 10.3724/SP.J.1004.2012.01485

Face Recognition Using Tensor Local Fisher Discriminant Analysis

  • 摘要: 子空间特征提取是人脸识别中的关键技术之一,结合局部Fisher判别分析技术和张量子空间分析技术的优点, 本文提出了一种新的张量局部Fisher判别分析(Tensor local Fisher discriminant analysis, TLFDA)子空间降维技术. 首先,通过对局部Fisher判别技术进行分析,调整了其类间散度目标泛函, 使算法的识别性能更高且时间复杂度更低;其次,引入张量型降维技术对输入数据进行双边投影变换而非单边投影, 获得了更高的数据压缩率;最后,采用迭代更新的方法计算最优的变换矩阵.通过ORL和PIE两个人脸库验证了所提算法的有效性.
  • [1] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720[2] He X F, Yan S C, Hu Y X, Niyogi P, Zhang H J. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340[3] Sugiyama M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research, 2007, 8(1): 1027-1061[4] Yan S C, Xu D, Zhang B Y, Zhang H J, Yang Q, Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51[5] Lei Y K, Ding Z G, Hu R X, Zhang S W, Jia W. Orthogonal local spline discriminant projection with application to face recognition. Pattern Recognition Letters, 2011, 23(4): 615 -625[6] Yan De-Qin, Liu Sheng-Lan, Li Yan-Yan. An embedding dimension reduction algorithm based on sparse analysis. Acta Automatica Sinica, 2011, 37(11): 1306-1312(闫德勤, 刘胜蓝, 李燕燕. 一种基于稀疏嵌入分析的降维方法. 自动化学报, 2011, 37(11): 1306-1312)[7] Gu X H, Gong W G, Yang L P. Regularized locality preserving discriminant analysis for face recognition. Neurocomputing, 2011, 74(17): 3036-3042[8] Li M, Yuan B Z. 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, 2005, 26(5): 527-532[9] Yu W W. Two-dimensional discriminant locality preserving projections for face recognition. Pattern Recognition Letters, 2009, 30(15): 1378-1383[10] Li Le, Zhang Yu-Jin. Linear projection-based non-negative matrix factorization. Acta Automatica Sinica, 2010, 36(1): 23-39 (李乐, 章毓晋. 基于线性投影结构的非负矩阵分解. 自动化学报, 2010, 36(1): 23-39)[11] Tang Ke-Wei, Liu Ri-Sheng, Du Hui, Su Zhi-Xun. A novel dimensionality reduction method based on tensor and lorentzian geometry. Acta Automatica Sinica, 2011, 37(9): 1151-1156 (唐科威, 刘日升, 杜慧, 苏志勋. 一种基于张量和洛仑兹几何的降维方法. 自动化学报, 2011, 37(9): 1151-1156)[12] He X F, Cai D, Niyogi P. Tensor Subspace Analysis. California: MIT Press, 2006. 499-507[13] Wang J G, Sung E, Yau W Y. Incremental two-dimensional linear discriminant analysis with applications to face recognition. Network and Computer Applications, 2010, 33(3): 314-322[14] Zhang Z, Chow W S. Tensor locally linear discriminative analysis. IEEE Signal Processing Letters, 2011, 18(11): 643 -646[15] Eschenauer H, Koski J, Osyczka A. Multicriteria Design Optimization. Berlin: Springer-Verlag, 1990. 88-92[16] Guan Z Y, Wang C, Chen Z G, Bu J J, Chen C. Efficient face recognition using tensor subspace regression. Neurocomputing, 2010, 73(13-15): 2744-2753
  • 加载中
计量
  • 文章访问数:  1809
  • HTML全文浏览量:  61
  • PDF下载量:  1178
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-07-04
  • 修回日期:  2012-02-20
  • 刊出日期:  2012-09-20

目录

    /

    返回文章
    返回