Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6894310 | Engineering Science and Technology, an International Journal | 2016 | 13 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
G.P. Hegde, M. Seetha, Nagaratna Hegde,