Two-dimensional joint local and nonlocal discriminant analysis-based 2D image feature extraction for deep learning

This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.

Download Full-text

Research on the two-dimensional face image feature extraction method

2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization ◽

10.1109/icssem.2012.6340720 ◽

2012 ◽

Cited By ~ 1

Author(s):

Qinsheng Du ◽

Jian Zhao ◽

Lijuan Shi ◽

Lirong Wang

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Face Image ◽

Image Feature ◽

Two Dimensional ◽

Image Feature Extraction ◽

Feature Extraction Method ◽

Dimensional Face

Download Full-text

UNCORRELATED PROJECTION DISCRIMINANT ANALYSIS AND ITS APPLICATION TO FACE IMAGE FEATURE EXTRACTION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001403002903 ◽

2003 ◽

Vol 17 (08) ◽

pp. 1325-1347 ◽

Cited By ~ 26

Author(s):

JIAN YANG ◽

JING-YU YANG ◽

ALEJANDRO F. FRANGI ◽

DAVID ZHANG

Keyword(s):

Feature Extraction ◽

Discriminant Analysis ◽

Face Image ◽

Desirable Property ◽

Experimental Results ◽

Image Feature ◽

Analysis Method ◽

Two Stage ◽

Image Feature Extraction ◽

Projection Analysis

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.

Download Full-text

Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.121-122.391 ◽

2010 ◽

Vol 121-122 ◽

pp. 391-398 ◽

Cited By ~ 2

Author(s):

Qi Rong Zhang ◽

Zhong Shi He

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Recognition Performance ◽

Recognition Rate ◽

Image Feature ◽

Two Dimensional ◽

Face Database ◽

Image Feature Extraction ◽

Locality Preserving Projection ◽

Locality Preserving

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.

Download Full-text