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 |
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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 | |
出版状态 | 已出版 - 8月 2008 |
已对外发布 | 是 |