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Face recognition using discriminant locality preserving projections based on maximum margin criterion. (English) Zbl 1209.68469
Summary: We propose a new discriminant locality preserving projections based on maximum margin criterion (DLPP/MMC). DLPP/MMC seeks to maximize the difference, rather than the ratio, between the locality preserving between-class scatter and locality preserving within-class scatter. DLPP/MMC is theoretically elegant and can derive its discriminant vectors from both the range of the locality preserving between-class scatter and the range space of locality preserving within-class scatter. DLPP/MMC can also derive its discriminant vectors from the null space of locality preserving within-class scatter when the parameter of DLPP/MMC approaches \(+ \infty \). Experiments on the ORL, Yale, FERET, and PIE face databases show the effectiveness of the proposed DLPP/MMC.

MSC:
68T10 Pattern recognition, speech recognition
Software:
Yale Face; FERET
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