Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis based subspace approach for expression recognition

This paper mainly focuses on dimensional reduction of fused dataset of holistic and geometrical face features vectors by solving singularity problem of linear discriminant analysis and maximizing the Fisher ratio in nonlinear subspace region with the preservation of local discriminative features. Th...

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Bibliographic Details
Published in:Engineering science and technology, an international journal an international journal, 2016-09, Vol.19 (3), p.1321-1333
Main Authors: Hegde, G.P., Seetha, M., Hegde, Nagaratna
Format: Article
Language:English
Subjects:
Discriminant analysis
Expression recognition
Feature extraction
Gabor filter
Subspace
Symmetrical weight
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Summary:This paper mainly focuses on dimensional reduction of fused dataset of holistic and geometrical face features vectors by solving singularity problem of linear discriminant analysis and maximizing the Fisher ratio in nonlinear subspace region with the preservation of local discriminative features. The combinational feature vector space is projected into low dimensional subspace using proposed Kernel Locality Preserving Symmetrical Weighted Fisher Discriminant Analysis (KLSWFDA) method. Matching score level fusion technique has been applied on projected subspace and combinational entire Gabor subspace is framed. Euclidean distance metric (L2) and support vector machine (SVM) classifier has been implemented to recognize and classify the expressions. Performance of proposed approach is evaluated and compared with state of art approaches. Experimental results on JAFFE, YALE and FD expression database demonstrate the effectiveness of the proposed approach.
ISSN:2215-0986
2215-0986
DOI:10.1016/j.jestch.2016.03.005