Face feature extraction and recognition via local binary pattern and two-dimensional locality preserving projection

2018 ◽  
Vol 78 (11) ◽  
pp. 14971-14987
Author(s):  
Lijian Zhou ◽  
Hui Wang ◽  
Wanquan Liu ◽  
Zhe-Ming Lu
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Two Dimensional ◽  
Locality Preserving Projection ◽  
Locality Preserving

Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

2010 ◽  
Vol 121-122 ◽  
pp. 391-398 ◽  
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.

Face Recognition Based on Rearranged Modular Two-Dimensional Locality Preserving Projection

2018 ◽  
Vol 32 (12) ◽  
pp. 1856016 ◽  
Author(s):  
Huxidan Jumahong ◽  
Gulnaz Alimjan
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Projection Algorithm ◽  
Two Dimensional ◽  
Spatial Structures ◽  
Locality Preserving Projection ◽  
Face Images ◽  
Locality Preserving ◽  
Local Block ◽  
Novel Algorithm

This paper proposes a novel algorithm for feature extraction for face recognition, namely the rearranged modular two-dimensional locality preserving projection (Rm2DLPP). In the proposed algorithm, the original images are first divided into modular blocks, then the subblocks are rearranged to form two-dimensional matrices and finally the two-dimensional locality preserving projection algorithm is applied directly on the arranged matrices. The advantage of the Rm2DLPP algorithm is that it can utilize the local block features and global spatial structures of 2D face images simultaneously. The performance of the proposed method is evaluated and compared with other face recognition methods on the ORL, AR and FERET databases. The experimental results demonstrate the effectiveness and superiority of the proposed approach.

Sparse two-dimensional discriminant locality-preserving projection (S2DDLPP) for feature extraction

2018 ◽  
Vol 23 (14) ◽  
pp. 5511-5518 ◽  
Author(s):  
Minghua Wan ◽  
Guowei Yang ◽  
Chengli Sun ◽  
Maoxi Liu
Keyword(s):  
Feature Extraction ◽  
Two Dimensional ◽  
Locality Preserving Projection ◽  
Locality Preserving

Feature Extraction of Hyperspectral Images of Weighted Spatial and Spectral Locality Preserving Projection (WSSLPP)

2017 ◽  
Vol 25 (1) ◽  
pp. 263-273 ◽  
Author(s):  
何 芳 HE Fang ◽  
王 榕 WANG Rong ◽  
于 强 YU Qiang ◽  
贾维敏 JIA Wei-min
Keyword(s):  
Feature Extraction ◽  
Hyperspectral Images ◽  
Locality Preserving Projection ◽  
Locality Preserving

A New Extension of Locality Preserving Projections for Image Feature Extraction

2012 ◽  
Vol 241-244 ◽  
pp. 1715-1718
Author(s):  
Guo Hong Huang
Keyword(s):  
Feature Extraction ◽  
Local Information ◽  
Image Feature ◽  
Two Dimensional ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Locality Preserving ◽  
Image Manifold ◽  
New Framework ◽  
Novel Algorithm

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.

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