Rough Set-Based Incremental Learning Approach to Face Recognition

2010 ◽  
pp. 356-365 ◽  
Author(s):  
Xuguang Chen ◽  
Wojciech Ziarko
Keyword(s):  
Face Recognition ◽  
Rough Set ◽  
Incremental Learning ◽  
Learning Approach

An Incremental Learning Approach for Updating Approximations in Rough Set Model over Dual Universes

2015 ◽  
Vol 30 (8) ◽  
pp. 923-947 ◽  
Author(s):  
Jie Hu ◽  
Tianrui Li ◽  
Hongmei Chen ◽  
Anping Zeng
Keyword(s):  
Rough Set ◽  
Incremental Learning ◽  
Learning Approach

A two-stage learning approach to face recognition

2017 ◽  
Vol 43 ◽  
pp. 21-29 ◽  
Author(s):  
Xiao Dong ◽  
Huaxiang Zhang ◽  
Jiande Sun ◽  
Wenbo Wan
Keyword(s):  
Face Recognition ◽  
Learning Approach ◽  
Two Stage

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.

Incremental learning of complete linear discriminant analysis for face recognition

2012 ◽  
Vol 31 ◽  
pp. 19-27 ◽  
Author(s):  
Gui-Fu Lu ◽  
Jian Zou ◽  
Yong Wang
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Incremental Learning ◽  
Linear Discriminant
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