Feature Extraction for Face Recognition Based on Gabor Filters and Two-Dimensional Locality Preserving Projections

2009 ◽  
Author(s):  
Yi-Chun Lee ◽  
Chin-Hsing Chen
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Gabor Filters ◽  
Two Dimensional ◽  
Locality Preserving Projections ◽  
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.

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