Locality Preserving Collaborative Representation for Face Recognition

2016 ◽  
Vol 45 (3) ◽  
pp. 967-979 ◽  
Author(s):  
Taisong Jin ◽  
Zhiling Liu ◽  
Zhengtao Yu ◽  
Xiaoping Min ◽  
Lingling Li
Keyword(s):  
Face Recognition ◽  
Collaborative Representation ◽  
Locality Preserving

Complete Discriminant Locality Preserving Projections for Face Recognition

2010 ◽  
Vol 21 (6) ◽  
pp. 1277-1286 ◽  
Author(s):  
Li-Ping YANG ◽  
Wei-Guo GONG ◽  
Xiao-Hua GU ◽  
Wei-Hong LI ◽  
Xing DU
Keyword(s):  
Face Recognition ◽  
Locality Preserving Projections ◽  
Locality Preserving

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

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.

Bagging null space locality preserving discriminant classifiers for face recognition

2009 ◽  
Vol 42 (9) ◽  
pp. 1853-1858 ◽  
Author(s):  
Liping Yang ◽  
Weiguo Gong ◽  
Xiaohua Gu ◽  
Weihong Li ◽  
Yanfei Liu
Keyword(s):  
Face Recognition ◽  
Null Space ◽  
Locality Preserving

Joint collaborative representation algorithm for face recognition

2018 ◽  
Vol 75 (5) ◽  
pp. 2304-2314
Author(s):  
Xincan Fan ◽  
Kaiyang Liu ◽  
Haibo Yi
Keyword(s):  
Face Recognition ◽  
Collaborative Representation
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