Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition
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.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
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