A Novel Approach for Face Recognition Based on Supervised Locality Preserving Projection and Maximum Margin Criterion

2009 ◽  
Author(s):  
Jun Kong ◽  
Shuyan Wang ◽  
Jianzhong Wang ◽  
Lintian Ma ◽  
Baowei Fu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Locality Preserving Projection ◽  
Maximum Margin ◽  
Locality Preserving ◽  
Novel Approach ◽  
Maximum Margin Criterion

scholarly journals An Improved Metric Learning Approach for Degraded Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Guofeng Zou ◽  
Yuanyuan Zhang ◽  
Kejun Wang ◽  
Shuming Jiang ◽  
Huisong Wan ◽  
...  
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
Computing Time ◽  
Feature Space ◽  
Projection Algorithm ◽  
Learning Approach ◽  
Learning Method ◽  
Matching Problem ◽  
Locality Preserving Projection ◽  
Locality Preserving

To solve the matching problem of the elements in different data collections, an improved coupled metric learning approach is proposed. First, we improved the supervised locality preserving projection algorithm and added the within-class and between-class information of the improved algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. Furthermore, we extended this algorithm to nonlinear space, and the kernel coupled metric learning method based on supervised locality preserving projection is proposed. In kernel coupled metric learning approach, two elements of different collections are mapped to the unified high dimensional feature space by kernel function, and then generalized metric learning is performed in this space. Experiments based on Yale and CAS-PEAL-R1 face databases demonstrate that the proposed kernel coupled approach performs better in low-resolution and fuzzy face recognition and can reduce the computing time; it is an effective metric method.

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.

Gabor tensor based face recognition using the boosted nonparametric maximum margin criterion

2017 ◽  
Vol 77 (7) ◽  
pp. 9055-9069 ◽  
Author(s):  
Jin Liu ◽  
Pengren A. ◽  
Qianqian Ge ◽  
Hang Zhao
Keyword(s):  
Face Recognition ◽  
Maximum Margin ◽  
Maximum Margin Criterion
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