Binary Two-Dimensional PCA

Author

Pang, Yanwei ; Tao, Dacheng ; Yuan, Yuan ; Li, Xuelong

Author_Institution

Sch. of Electron. Inf. Eng., Tianjin Univ., Tianjin

Volume

38

Issue

4

fYear

2008

Firstpage

1176

Lastpage

1180

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

Haar transforms; biometrics (access control); image recognition; image resolution; principal component analysis; testing; Haarlike bases; binary 2D principal component analysis; biometrics recognition research; high-resolution image data sets; image decomposition; testing procedures; training procedures; 2-D PCA (2DPCA); Face recognition; Haarlike bases; principal component analysis (PCA); subspace selection; Algorithms; Biometry; Face; Humans; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Principal Component Analysis; Signal Processing, Computer-Assisted;

fLanguage

English

Journal_Title

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

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/TSMCB.2008.923151

Filename

4554035