Papers by Author: Qi Rong Zhang

Paper TitlePage

Authors: Qi Rong Zhang, Jia Nan Gu, Ming Fu Zhang
Abstract: Li et al. [Pattern Recognition 41 (2008) 3287 -- 329 proposed the constrained maximum variance mapping method. The CMVM is globally maximizing the distances between different manifolds. We find out that globally minimizing the distances between the same manifolds can have better recognition than CMVM method on the Yale face database, ORL face database and UMIST face database. Hence we propose to use an inverse constrained maximum variance mapping method (ICMVM) which can be seen as the inverse Laplacian Fisher discriminate criteria. Experiment results suggest that this new approach performs well.
452
Authors: Qi Rong Zhang, Zhong Shi He
Abstract: 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.
391
Authors: Qi Rong Zhang
Abstract: In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional parameter principal component analysis (2DPPCA). Two-dimensional principal component analysis (2DPCA) is widely used in face recognition. We further study on the basis of 2DPCA. This proposed method is to add a parameter to images samples matrix in the image covariance matrix. Extensive experiments are performed on FERET face database and CMU PIE face database. The 2DPPCA method achieves better face recognition performance than PCA, 2DPCA, especially on the CMU PIE face database.
1838
Showing 1 to 3 of 3 Paper Titles