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Received: 2008-06-18

Revision Accepted: 2008-10-23

Crosschecked: 2009-06-10

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.8 P.1140-1152

http://doi.org/10.1631/jzus.A0820460


Feature selection for face recognition: a memetic algorithmic approach


Author(s):  Dinesh KUMAR, Shakti KUMAR, C. S. RAI

Affiliation(s):  Department of Computer Science & Engineering, Guru Jambheshwar University of Science & Technology, Haryana 125001, India; more

Corresponding email(s):   dinesh_chutani@yahoo.com, shakti@istk.org, csrai_ipu@yahoo.com

Key Words:  Face recognition, Memetic algorithm (MA), Principal component analysis (PCA), Linear discriminant analysis (LDA), Kernel principal component analysis (KPCA), Feature selection


Dinesh KUMAR, Shakti KUMAR, C. S. RAI. Feature selection for face recognition: a memetic algorithmic approach[J]. Journal of Zhejiang University Science A, 2009, 10(8): 1140-1152.

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author="Dinesh KUMAR, Shakti KUMAR, C. S. RAI",
journal="Journal of Zhejiang University Science A",
volume="10",
number="8",
pages="1140-1152",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820460"
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%T Feature selection for face recognition: a memetic algorithmic approach
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%A Shakti KUMAR
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DOI - 10.1631/jzus.A0820460


Abstract: 
The eigenface method that uses principal component analysis (PCA) has been the standard and popular method used in face recognition. This paper presents a PCA - memetic algorithm (PCA-MA) approach for feature selection. PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection. Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier. It was found that as far as the recognition rate is concerned, PCA-MA completely outperforms the eigenface method. We compared the performance of PCA extended with genetic algorithm (PCA-GA) with our proposed PCA-MA method. The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method. We further extended linear discriminant analysis (LDA) and kernel principal component analysis (KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features. This paper also compares the performance of PCA-MA, LDA-MA and KPCA-MA approaches.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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