Symbolic factorial discriminant analysis for face recognition under variable lighting

2006 ◽  
Author(s):  
P.S. Hiremath ◽  
C.J. Prabhakar
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Factorial Discriminant Analysis

SYMBOLIC FACTORIAL DISCRIMINANT ANALYSIS FOR ILLUMINATION INVARIANT FACE RECOGNITION

2008 ◽  
Vol 22 (03) ◽  
pp. 371-387 ◽  
Author(s):  
P. S. HIREMATH ◽  
C. J. PRABHAKAR
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Minimum Distance ◽  
Face Representation ◽  
Lighting Conditions ◽  
Illumination Changes ◽  
Effective Performance ◽  
Interval Type ◽  
Human Faces ◽  
Factorial Discriminant Analysis

In this paper, a new appearance-based technique called symbolic factorial discriminant analysis (symbolic FDA) is explored for face representation and recognition under varying illumination conditions. In the past few years, many appearance-based methods have been proposed to model image variations of human faces under different lighting conditions using single valued variables to represent the facial features. In the proposed symbolic factorial discriminant analysis method, we extract interval type discriminating features, which are robust to illumination changes. The minimum distance classifier with symbolic dissimilarity measure is used for classification. The proposed method has been successfully tested for face recognition using three databases, namely, Yale Face database B, CMU PIE database and Harvard database. The experimental results have demonstrated the effective performance of this method.

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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