Binary two-dimensional PCA

Yanwei Pang; Dacheng Tao; Yuan Yuan; Xuelong Li

doi:10.1109/TSMCB.2008.923151

Binary two-dimensional PCA

Yanwei Pang^*, Dacheng Tao, Yuan Yuan, Xuelong Li

^*Corresponding author for this work

Computer Science Research Group

Research output: Contribution to journal › Article › peer-review

Abstract

Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.

Original language	English
Pages (from-to)	1176-1180
Number of pages	5
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	38
Issue number	4
DOIs	https://doi.org/10.1109/TSMCB.2008.923151
Publication status	Published - 1 Aug 2008

Keywords

2-D PCA (2DPCA)
Face recognition
Haarlike bases
Principal component analysis (PCA)
Subspace selection

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10.1109/TSMCB.2008.923151

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title = "Binary two-dimensional PCA",

abstract = "Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.",

keywords = "2-D PCA (2DPCA), Face recognition, Haarlike bases, Principal component analysis (PCA), Subspace selection",

author = "Yanwei Pang and Dacheng Tao and Yuan Yuan and Xuelong Li",

year = "2008",

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AU - Pang, Yanwei

AU - Tao, Dacheng

AU - Yuan, Yuan

AU - Li, Xuelong

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N2 - Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.

AB - Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.

KW - 2-D PCA (2DPCA)

KW - Face recognition

KW - Haarlike bases

KW - Principal component analysis (PCA)

KW - Subspace selection

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JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

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ER -

Binary two-dimensional PCA

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