Volume 11 Number 12 (Dec. 2016)
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JSW 2016 Vol.11(12): 1172-1181 ISSN: 1796-217X
doi: 10.17706/jsw.11.12.1172-1181

Maximal Margin Local Preserving Median Fisher Discriminant Analysis for Face Recognition

Xingzhu Liang*, Yu’e Lin
School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China.

Abstract—Median Fisher Discriminator(MFD) used the class median vector is more effective than Linear discriminant analysis(LDA). However, MFD only captures global geometrical structure information of the data and ignores the geometrical structure information of local data point. In this paper, we introduce a linear approach, called Maximal Margin Local Preserving Median Fisher Discriminant Analysis (MMLPMFDA). MMLPMFDA models the geometrical structure and variability of the local neighborhoods by constructing two adjacency graphs over the training data, and then incorporates the geometry and variability into the objective function of the MFD. In order to solve the small sample size problem, the objective function in a form of the difference is adopted. Finally, experiments on the ORL, YALE and AR face databases show the effectiveness of the proposed approach.

Index Terms—Global, local, median fisher discriminator, the small sample size problem.


Cite: Xingzhu Liang, Yu’e Lin, "Maximal Margin Local Preserving Median Fisher Discriminant Analysis for Face Recognition," Journal of Software vol. 11, no. 12, pp. 1172-1181, 2016.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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