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Face recognition using kernel principal component analysis

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Face recognition using kernel principal component analysis. / Kim, Kwang In; Jung, Keechul; Kim, Hang Joon.

In: IEEE Signal Processing Letters, Vol. 9, No. 2, 2002, p. 40-42.

Research output: Contribution to journalJournal article

Harvard

Kim, KI, Jung, K & Kim, HJ 2002, 'Face recognition using kernel principal component analysis', IEEE Signal Processing Letters, vol. 9, no. 2, pp. 40-42. https://doi.org/10.1109/97.991133

APA

Kim, K. I., Jung, K., & Kim, H. J. (2002). Face recognition using kernel principal component analysis. IEEE Signal Processing Letters, 9(2), 40-42. https://doi.org/10.1109/97.991133

Vancouver

Kim KI, Jung K, Kim HJ. Face recognition using kernel principal component analysis. IEEE Signal Processing Letters. 2002;9(2):40-42. https://doi.org/10.1109/97.991133

Author

Kim, Kwang In ; Jung, Keechul ; Kim, Hang Joon. / Face recognition using kernel principal component analysis. In: IEEE Signal Processing Letters. 2002 ; Vol. 9, No. 2. pp. 40-42.

Bibtex

@article{fed295d30bc0476db68eaa2e27aef6a6,
title = "Face recognition using kernel principal component analysis",
abstract = "A kernel principal component analysis (PCA) was previously proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This article adopts the kernel PCA as a mechanism for extracting facial features. Through adopting a polynomial kernel, the principal components can be computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.",
author = "Kim, {Kwang In} and Keechul Jung and Kim, {Hang Joon}",
year = "2002",
doi = "10.1109/97.991133",
language = "English",
volume = "9",
pages = "40--42",
journal = "IEEE Signal Processing Letters",
issn = "1070-9908",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Face recognition using kernel principal component analysis

AU - Kim, Kwang In

AU - Jung, Keechul

AU - Kim, Hang Joon

PY - 2002

Y1 - 2002

N2 - A kernel principal component analysis (PCA) was previously proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This article adopts the kernel PCA as a mechanism for extracting facial features. Through adopting a polynomial kernel, the principal components can be computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.

AB - A kernel principal component analysis (PCA) was previously proposed as a nonlinear extension of a PCA. The basic idea is to first map the input space into a feature space via nonlinear mapping and then compute the principal components in that feature space. This article adopts the kernel PCA as a mechanism for extracting facial features. Through adopting a polynomial kernel, the principal components can be computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.

U2 - 10.1109/97.991133

DO - 10.1109/97.991133

M3 - Journal article

VL - 9

SP - 40

EP - 42

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

SN - 1070-9908

IS - 2

ER -