Face Recognition Using KPCA and KFDA

Hong Mei Li; Dong Ming Zhou; Ren Can  Nie; Xiang Li; Hai Ying Deng

doi:10.4028/www.scientific.net/AMM.380-384.3850

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Face Recognition Using KPCA and KFDA

Face Recognition Using KPCA and KFDA

Abstract:

KPCA extracting principal component with nonlinear method is an improved PCA. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. The method of KFDA is equivalent to KPCA plus LDA. KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. The KPCA and KFDA have been got widely used in feature extraction and face recognition. In this paper, the method of KPCA and KFDA is analyzed and their nature is revealed. Finally, the effectiveness of the algorithm is verified using the ORL database.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

3850-3853

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.3850

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Cite this paper

Online since:

August 2013

Authors:

Hong Mei Li, Dong Ming Zhou, Ren Can Nie, Xiang Li, Hai Ying Deng

Keywords:

Face Recognition, Feature Extraction, KFDA, KPCA, LDA

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References

[1] Weilin Huang, Hujun Yin. On nonlinear dimensionality reduction for face recognition. Image and Vision Computing, 2012, 30(4-5): 355-366.

DOI: 10.1016/j.imavis.2012.03.004

Google Scholar

[2] Guan-Chun, Chun-Yi Lin. PCA based immune networks for human face recognition. Applied Soft Computing, 2011, 11(2): 1743-1752.

DOI: 10.1016/j.asoc.2010.05.017

Google Scholar

[3] Yanmei Wang, Yanzhu Zhang. The facial expression recognition based on KPCA. Intelligent Control and Information Processing. 2010, 365-368.

Google Scholar

[4] Xin Shu, Yao Gao. Efficient linear discriminant analysis with locality preserving for face recognition. Pattern Recognition, 2012, 45(5): 1892-1898.

DOI: 10.1016/j.patcog.2011.11.012

Google Scholar

[5] Weilin Huang, Hujun Yin. On nonlinear dimensionality reduction for face recognition. Image and Vision Computing, 2012, 30(4-5): 355-366.

DOI: 10.1016/j.imavis.2012.03.004

Google Scholar

[6] Jian Yang, Zhong Jin Jingyu Yang. Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognition. 2004, (37): 2097-2100.

DOI: 10.1016/j.patcog.2003.10.015

Google Scholar

[7] Shekar B H. Face recognition using kernel entropy component analysis. Neurocomputing, 2011, 74(6): 1053-1057.

DOI: 10.1016/j.neucom.2010.10.012

Google Scholar