Behavior classification algorithm based on enhanced gait energy image and two-dimensional locality preserving projection

2011 ◽  
Vol 31 (3) ◽  
pp. 721-723 ◽  
Author(s):  
Chun-li LIN ◽  
Ke-jun WANG ◽  
Yue LI
Keyword(s):  
Classification Algorithm ◽  
Two Dimensional ◽  
Locality Preserving Projection ◽  
Gait Energy Image ◽  
Locality Preserving

2DRLPP: Robust two-dimensional locality preserving projection with regularization

2019 ◽  
Vol 169 ◽  
pp. 53-66 ◽  
Author(s):  
Wei-Jie Chen ◽  
Chun-Na Li ◽  
Yuan-Hai Shao ◽  
Ju Zhang ◽  
Nai-Yang Deng
Keyword(s):  
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.

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