Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

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In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

Info:

Periodical:

Advanced Materials Research (Volumes 121-122)

Edited by:

Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo

Pages:

391-398

DOI:

10.4028/www.scientific.net/AMR.121-122.391

Citation:

Q. R. Zhang and Z. S. He, "Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition", Advanced Materials Research, Vols. 121-122, pp. 391-398, 2010

Online since:

June 2010

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Price:

$38.00

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