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

科研成果: 期刊稿件 › 文章 › 同行评审

77 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	1176-1180
页数	5
期刊	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
卷	38
期	4
DOI	https://doi.org/10.1109/TSMCB.2008.923151
出版状态	已出版 - 8月 2008
已对外发布	是

访问文件

10.1109/TSMCB.2008.923151

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{4b029964c13349a29cb78c9d28549d93,

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",

month = aug,

doi = "10.1109/TSMCB.2008.923151",

language = "英语",

volume = "38",

pages = "1176--1180",

journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",

issn = "1083-4419",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

TY - JOUR

T1 - Binary two-dimensional PCA

AU - Pang, Yanwei

AU - Tao, Dacheng

AU - Yuan, Yuan

AU - Li, Xuelong

PY - 2008/8

Y1 - 2008/8

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

UR - http://www.scopus.com/inward/record.url?scp=49049102521&partnerID=8YFLogxK

U2 - 10.1109/TSMCB.2008.923151

DO - 10.1109/TSMCB.2008.923151

M3 - 文章

C2 - 18632407

AN - SCOPUS:49049102521

SN - 1083-4419

VL - 38

SP - 1176

EP - 1180

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

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

IS - 4

ER -

Binary two-dimensional PCA

摘要

访问文件

其它文件与链接

指纹

引用此