Title :
Binary Two-Dimensional PCA
Author :
Pang, Yanwei ; Tao, Dacheng ; Yuan, Yuan ; Li, Xuelong
Author_Institution :
Sch. of Electron. Inf. Eng., Tianjin Univ., Tianjin
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;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2008.923151
