Two-dimensional locality discriminant preserving projections for face recognition

被引：2
|
作者：
Zhang, Qirong [1 ]
He, Zhongshi [1 ]
机构：
[1] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
来源：
NANOTECHNOLOGY AND COMPUTER ENGINEERING | 2010年 / 121-122卷
基金：
国家高技术研究发展计划(863计划);
关键词：
two-dimensional locality discriminant preserving projections (2DLDPP); two-dimensional locality preserving projections (2DLPP); modified maximizing margin criterion (MMMC); face recognition; DIMENSIONALITY REDUCTION; LAPLACIAN EIGENMAPS; EFFICIENT; REPRESENTATION; PCA;
D O I：
10.4028/www.scientific.net/AMR.121-122.391
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：391 / +
页数：2
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