Wavelet Feature-Based Nonlinear Feature Extraction Technique

Chen Cai-kou; Gao Xiu-mei; Guo L; i Yang Jing-yu

Volume 27 Issue 2

Feb. 2005

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2005 > 27(2): 290-293

Chen Cai-kou, Gao Xiu-mei, Guo L, i Yang Jing-yu. Wavelet Feature-Based Nonlinear Feature Extraction Technique[J]. Journal of Electronics & Information Technology, 2005, 27(2): 290-293.

Citation:

Chen Cai-kou, Gao Xiu-mei, Guo L, i Yang Jing-yu. Wavelet Feature-Based Nonlinear Feature Extraction Technique[J]. Journal of Electronics & Information Technology, 2005, 27(2): 290-293.

Chen Cai-kou, Gao Xiu-mei, Guo L, i Yang Jing-yu. Wavelet Feature-Based Nonlinear Feature Extraction Technique[J]. Journal of Electronics & Information Technology, 2005, 27(2): 290-293.

Citation:

Chen Cai-kou, Gao Xiu-mei, Guo L, i Yang Jing-yu. Wavelet Feature-Based Nonlinear Feature Extraction Technique[J]. Journal of Electronics & Information Technology, 2005, 27(2): 290-293.

PDF( 1146 KB)

Wavelet Feature-Based Nonlinear Feature Extraction Technique

Chen Cai-kou^{①②;高秀梅},
Gao Xiu-mei,
Guo L,
i Yang Jing-yu

Received Date: 2003-09-24
Rev Recd Date: 2004-05-07
Publish Date: 2005-02-19

Abstract

Abstract

The paper developes a novel nonlinear feature extraction method based on wavelet features. Its main idea is that wavelet transform is first employed to preprocess the original training images before the nonlinear mapping and three groups of wavelet features: lowest frequency subimage, horizontal detail and vertical detail, are derived respectively, What follows, Kernel Fisher Discriminant Analysis(KFDA) is performed on three classes of wavelet features. Three final discriminant feature vectors are obtained, from which a feature fusing method is developed. Finally, The experimental results on ORL face databases indicate that the proposed method is more effective than the current KFDA. And, more importantly, its consumed time in feature extraction is only one thirteenth of that of KFDA. Moreover, the experiments also demonstrate that this method is robust in uncontrolled lighting condition.

FullText(HTML)

References(1)

References

高西奇,周洪祥,何振亚.基于小波变换的主元分析人脸图像识别.东南大学学报,1996,26(2):137-141.[2]Sergent J. Microfenesis of face perception. in: H.D.EIIis. Aspects of Face Processing, Nijhoff, Dordrecht, 1986:80 - 104.[3]Liu C J, Wechsler Harry. A shape- and texture-based enhanced Fisher classifier for face recognition[J].IEEE Trans. on Image Processing.2001, 10(4):598-[4]Scholkopf Bernhard, Smola Alexander, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem[J].Neural Computation.1998, 10:1299-[5]Mika Sebastian, Ratsch Gunnar, Weston Jason, Scholkopf Bernhard, et al.. Fisher diseriminant analysis with kernels. In IEEE workshop on Networks for Signal Processing, 1999:41-48.[6]Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach[J].Neural Computation.2000, 12( 10):2385-[7]Roth Volker, Steinhage Volker. Nonlinear discriminant analysis using kernel functions. In Proc. of Neural Information Processing systems, Denver, Nov. 1999:568 - 574.[8]Yang M H. Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods, Proc. of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition(RGR02), Washington DC, May 2002:215 - 220.[9]Vapnik V. The Nature of Statistical Learning Theory. New York:Springer-Verlag, 1995:302 - 309.[10]程云鹏.矩阵论.西安:西北工业大学出版社,1999:294-302.

Relative Articles

Supplements(0)

Cited By

Proportional views