Face recognition using discriminant locality preserving projections based on maximum margin criterion

Authors:
Gui-Fu Lu;Zhong Lin;Zhong Jin
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China and Department of Computer Science and Engineering, AnHui University of Tech ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
Venue:
Pattern Recognition
Year:
2010

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Abstract

In this paper, 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 +~. Experiments on the ORL, Yale, FERET, and PIE face databases show the effectiveness of the proposed DLPP/MMC.