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多阶矩阵组合LDA及其在人脸识别中的应用 被引量：3

Modified LDA based on linear combination of k-order matrices and its application to face recogniton
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摘要 线性判别分析(LDA)是一种普遍用于特征提取的线性分类方法。但将LDA直接用于人脸识别会遇到小样本问题和秩限制问题。为了解决以上问题,提出一种基于多阶矩阵组合的LDA算法——MLDA。该算法重新定义了传统LDA中的类内离散度矩阵Sw,使传统Fisher准则具有更好的健壮性和适应性。若干人脸数据库上的比较实验证明了MLDA的有效性。 Linear Discriminant Analysis（LDA） is one of the most popular linear classification techniques for feature extraction,but when dealing with face recognition, it will meet two problems：small sample size and rank limitation.In order to solve these two problems, this paper presents a modified LDA based on linear combination of k-order matrices-MLDA.MLDA redefines within-class scatter matrix Sw in order to make the traditional Fisher criterion get much more suitable to other situations.Experiments on different face databases verify the effectiveness of MLDA.
作者 刘忠宝 王士同
机构地区 江南大学信息学院 山西大学商务学院信息工程系
出处 《计算机工程与应用》 CSCD 北大核心 2011年第12期152-155,共4页 Computer Engineering and Applications
基金 国家863资助项目(No.2007AA1Z158 No.2006AA10Z313) 2006年江苏省6大人才高峰计划资助项目 2008江苏省研究生创新计划课题
关键词 线性判别分析(LDA) 类内离散度矩阵 多阶矩阵组合 人脸识别 Linear Discriminant Analysis（LDA） within-class scatter matrix linear combination of k-order matrices face recognitibn
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献14

  • 1Chellappa R,Wilson C,Sirohey S.Human and machine recognition of faces:A surway[J].Proc IEEE,1995,83(5):705-741.
  • 2Tplba A S,El-Bas A H,El-Harby A A.Face recognition:A literature review[J].J of Signal Processing,2005,2(1):88-103.
  • 3Turk M,Pentland A.Eigenfaces for recognition[J].J of Cognitive Neuroscience,1991,3(1):71-86.
  • 4Swets D,Weng J.Using discriminant eiganfeatures for image retrieval[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(8):831-836.
  • 5Yu H,Yang J.A direct LDS algorithm for high-dimensional data with application to face recognition[J].Pattern Recognition,2001,34:2067-2070.
  • 6Hong Z Q,Yang J Y.Optimal diecriminant plane for a small number of samples and design method of classifier on the plane[J].Pattern Recognition,1991,24(4):317-324.
  • 7杨健,杨静宇,叶晖.Fisher线性鉴别分析的理论研究及其应用[J].自动化学报,2003,29(4):481-493. 被引量：97
  • 8边肇棋 张学工.模式识别[M].北京：清华大学出版社,2000..
  • 9Belhumeur P N.Eigenfaces vs Fisherfaces:Recognition using class specific linear projection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
  • 10Etemad K,Chellappa R.Discriminant analysis for recognition of human face images[J].Journal of the Optical Society of America A:Optics Image Science and Vision,1997,14(8):1724-1733.

二级参考文献26

  • 1[1]Wilks S S. Mathematical Statistics. New York: Wiley Press, 1962. 577～578
  • 2[2]Duda R, Hart P. Pattern Classification and Scene Analysis. New York: Wiley Press, 1973
  • 3[3]Daniel L Swets, John Weng. Using discriminant eigenfeatures for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996,18(8): 831～836
  • 4[4]Belhumeur P N. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711～720
  • 5[5]Cheng Jun Liu, Harry Wechsler. A shape- and texture-based enhanced Fisher classifier for face recognition. IEEE Transactions on Image Processing, 2001, 10(4): 598～608
  • 6[6]Foley D H, Sammon J W Jr. An optimal set of discriminant vectors. IEEE Transactions on Computer, 1975, 24(3): 281～289
  • 7[7]Tian Q. Image classification by the Foley-Sammon transform. Optical Engineering, 1986, 25(7): 834～839
  • 8[8]Duchene J, Leclercq S. An optimal Transformation for discriminant and principal component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988,10(6): 978～983
  • 9[9]Zhong Jin, Yang J Y, Hu Z S, Lou Z. Face Recognition based on uncorrelated discriminant transformation. Pattern Recognition, 2001,33(7): 1405～1416
  • 10[10]Yang Jian, Yang Jing-Yu, Jin Zhong. An apporach of optimal discriminatory feature extraction and its application in image recognition. Journal of Computer Research and Development, 2001,38(11):1331～1336(in Chinese)

共引文献181

同被引文献33

  • 1Martinez A M and Kak A C. PCA versus LDA[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.
  • 2Alibeigi M, Hashemi S, and Hamzeh A. DBFS: an effective density based feature selection scheme for small sample size and high dimensional imbalanced data sets[J]. Data Knowledge Engineering, 2012, 81/82: 67-103.
  • 3Friedman H. Regularized discriminant analysis[J]. Journal of the American Statistical Association, 1989, 84(405): 165 175.
  • 4Li M and Yuan B. 2D-LDA: a novel statistical linear discriminant analysis for image matrix[J]. Pattern Recognition Letters, 2005, 26(5): 527-532.
  • 5Ye J P and Xiong T. Computational and theoretical analysis of null space and orthogonal linear discriminant analysis[J]. Journal of Machine Learning Research, 2006, 7: 1183-1204.
  • 6Yu H and Yang J. A direct LDA algorithm for high- dimensional data with application to face recognition [J]. Pattern Recognition, 2001, 34(11): 2067-2070.
  • 7Wan M H, Lai Z H, and Jin Z. Feature extraction using two-dimensional local graph embedding based on maximum margin criterion[J]. Applied Mathematics and Computation, 2011, 217(23): 9659-9668.
  • 8Ji S W and Ye J P. Generalized linear discriminant analysis: a unified framework and efficient model selection[J]. IEEE Transactions on Neural Networks, 2008, 19(10): 1768-1782.
  • 9Chen L F, Liao H Y M, Ko M T, et al. A new LDA-based face recognition system which can solve the small sample size problem[J]. Pattern Recognition, 2000, 33(10): 1713-1726.
  • 10Belhumeur P N, Hespanha J P, and Kriegman D J. Eiegnfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.

引证文献3

二级引证文献17

计算机工程与应用
2011年 第12期

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