A Optimal Kernel Fisher Nonlinear Discriminant Analysis Method and Applied on Face Recognition

2008 ◽  
Author(s):  
Zhi-Guang Li ◽  
Fu-Long Wang ◽  
Wei-Zhao Zhu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Analysis Method ◽  
Discriminant Analysis Method ◽  
Optimal Kernel ◽  
Nonlinear Discriminant Analysis

An Improved Linear Discriminant Analysis Method and its Application to Face Recognition

2014 ◽  
Vol 556-562 ◽  
pp. 4825-4829 ◽  
Author(s):  
Kai Li ◽  
Peng Tang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Method ◽  
Analysis Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Discriminant Analysis Method

Linear discriminant analysis (LDA) is an important feature extraction method. This paper proposes an improved linear discriminant analysis method, which redefines the within-class scatter matrix and introduces the normalized parameter to control the bias and variance of eigenvalues. In addition, it makes the between-class scatter matrix to weight and avoids the overlapping of neighboring class samples. Some experiments for the improved algorithm presented by us are performed on the ORL, FERET and YALE face databases, and it is compared with other commonly used methods. Experimental results show that the proposed algorithm is the effective.

Sign in / Sign up

Export Citation Format

Share Document