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

Engineering Science and Technology, an International Journal

M. Seetha¹  G.P. Hegde²  Nagaratna Hegde³ 

[1] Dept .of CSE, GNITS, Hyderabad, India;Dept. CSE, SDMIT, Ujire affiliated to VTU Belgaum, Karnataka State, India;Dept. of CSE, VCE, Hyderabad, India;

关键词: Discriminant analysis;    Gabor filter;    Expression recognition;    Feature extraction;    Subspace;    Symmetrical weight;   

DOI  :  10.1016/j.jestch.2016.03.005

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown