Kernel Uncorrelated Space Algorithm and Its Application to Face Recognition

Lin Yu-e; Gu Guo-chang; Liu Hai-bo; Shen Jing

doi:10.3724/SP.J.1146.2008.00718

Volume 31 Issue 8

Dec. 2010

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Lin Yu-e, Gu Guo-chang, Liu Hai-bo, Shen Jing. Kernel Uncorrelated Space Algorithm and Its Application to Face Recognition[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1812-1815. doi: 10.3724/SP.J.1146.2008.00718

Citation:

Lin Yu-e, Gu Guo-chang, Liu Hai-bo, Shen Jing. Kernel Uncorrelated Space Algorithm and Its Application to Face Recognition[J]. Journal of Electronics & Information Technology, 2009, 31(8): 1812-1815. doi: 10.3724/SP.J.1146.2008.00718

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Kernel Uncorrelated Space Algorithm and Its Application to Face Recognition

doi: 10.3724/SP.J.1146.2008.00718

Received Date: 2008-06-02
Rev Recd Date: 2009-03-30
Publish Date: 2009-08-19

Abstract

Abstract

Uncorrelated space algorithm is a fast method for the uncorrelated discriminant vectors extraction，but it may encounter the small size samples problem when it is applied to face recognition task. In addition, it is only a linear feature extraction technique. In this paper，kernel uncorrelated space algorithm is proposed. The key of the proposed algorithm is to how to compute the uncorrelated space in the higher dimensional feature space. As to this problem, a very simple and easy method is proposed, which originates from the eigenface that transforms the computation of the high order matrix into the computation of the low order matrix, and then the actual computation of the uncorrelated space in the higher dimensional feature space is reduced to a standard eignenvalue problem. In addition, the proposed algorithm can effectively overcome small size samples problem. The numerical experiments on facial databases of ORL show that the proposed method is effective and feasible.

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References(1)

References

Jin Z, Yang J, and Hu Z, et al.. Face recognition based on theuncorrelated discrimination transformation [J]. PatternRecognition, 2001, 34(7): 1405-1416.[2]陈绵书, 陈贺新, 刘伟. 一种新的求解无相关鉴别矢量集方法[J]. 计算机学报, 2004, 27(7): 913-917.Chen Mian-shu, Chen He-xin, and Liu Wei. A new method forresolving the uncorrelated set of discriminant vectors [J].Chinese Journal of Computers, 2004, 27(7): 913-917.[3]Sch.lkopf B.[J].Smola A, and Mller K R. Nonlinear componentanalysis as a kernel eigenvalue problem [R]. Max-Planck-Institut fr biologische Kybernetik, Technical Report.1996,44:-[4]Bach F R and Jordan M I. Kernel independent componentanalysis[R]. Department of Computer Science, University ofCalifornia, Technical Report UCB/CSD-01-1166, 2001.[5]Mika S, Ratsch G, and Weston J, et al.. Fisher discriminantanalysis with kernels [C].Proceedings of IEEE InternationalWorkshop on Neural Networks for Signal Processing.Madison, Wisconsin, August 1999: 41-48.[6]Yang J, Frangi A, and Yang J Y, et al.. KPCA plus LDA: Acomplete kernel Fisher discriminant framework for featureextraction and recognition [J].IEEE Transactions on PatternAnalysis Machine Intelligence.2005, 27(2):230-244[7]梁志贞, 施鹏飞. 核不相关鉴别分析以及它在字符识别中的应用[J]. 计算机研究与发展, 2006, 43(1): 132-137.Liang Zhi-zhen and Shi Peng-fei. Kernel uncorrelateddiscriminant analysis and its application to handwrittencharacter recognition [J]. Journal of Computer Research andDevelopment, 2006, 43(1): 132-137.[8]Turk M and Pentland A. Eigenfaces for recognition[J].Journal of Cognitive Neuroscience.1991, 3(1):71-86[9]Liu K and Yang J Y. An efficient algorithm for Foley-Sammon optimal set of discriminant vectors by algebraicmethod[J].International Journal of Pattern Recognition andArtificial Intelligence.1992, 6(5):817-829[10]Zheng W M and Zhao L. Foley-Sammon optimaldiscriminant vectors using kernel approach [J].IEEETransactions on Neural Networks.2005, 16(1):1-9

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