Locality projection discriminant analysis with an application to face recognition

2010 ◽  
Vol 49 (7) ◽  
pp. 077201 ◽  
Author(s):  
Yanmin Niu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis

scholarly journals Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wen-Sheng Chen ◽  
Chu Zhang ◽  
Shengyong Chen
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Radial Basis Function ◽  
Linear Discriminant Analysis ◽  
Fractional Order ◽  
Basis Function ◽  
Geometric Distribution ◽  
Superior Performance ◽  
Linear Discriminant ◽  
Radial Basis

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance.

scholarly journals Coupled Discriminant Analysis for Heterogeneous Face Recognition

2012 ◽  
Vol 7 (6) ◽  
pp. 1707-1716 ◽  
Author(s):  
Zhen Lei ◽  
Shengcai Liao ◽  
Anil K. Jain ◽  
Stan Z. Li
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Heterogeneous Face Recognition

Semi-supervised Discriminant Analysis and Sparse Representation-based self-training for Face Recognition

Optik ◽  
2014 ◽  
Vol 125 (9) ◽  
pp. 2170-2174 ◽  
Author(s):  
Jiang Jiang ◽  
Haitao Gan ◽  
Liangwei Jiang ◽  
Changxin Gao ◽  
Nong Sang
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Sparse Representation
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