Binary Two-Dimensional PCA

Authors:
Yanwei Pang;Dacheng Tao;Yuan Yuan;Xuelong Li
Affiliations:
Sch. of Electron. Inf. Eng., Tianjin Univ., Tianjin;-;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2008

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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.