NDLI: Relevance weighted $(2D)^{2}LDA$ image projection technique for face recognition application

Content Provider	IEEE Xplore Digital Library
Author	Sanayha, W. Rangsanseri, Y.
Copyright Year	2009
Description	Author affiliation: Department of Telecommunications Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand (Sanayha, W.; Rangsanseri, Y.)
Abstract	In this paper, a novel image projection technique for face recognition application is proposed based on the Linear Discriminant Analysis (LDA) combining with relevance weighted. The projection technique is performed through 2-directional and 2-dimensional LDA or $(2D)^{2}LDA$ approach which simultaneously work in row and column directions to overcome the “small sample size” problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and relevance weighted is also used in the within-class scatter matrix in order to weigh the information for solving confusable data in these classes. This technique is called the Relevance Weighted 2-Dimensional and 2-Directional LDA or $RW(2D)^{2}LDA$ which are used for a more accurate discriminant decision than the one that is produced by the conventional LDA, or 2DLDA. The proposed technique has been successfully tested on three face databases. Experimental results indicate that the proposed $RW(2D)^{2}LDA$ algorithm is more computationally efficient than the conventional algorithms in view of a little features and less times. It can also improve performance, and takes maximum recognition rate more over 97%.
Starting Page	663
Ending Page	667
File Size	299535
Page Count	5
File Format	PDF
ISBN	9781424433872
DOI	10.1109/ECTICON.2009.5137136
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-06
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition High performance computing Scattering Image representation Vectors Spatial databases Linear discriminant analysis Matrix decomposition Principal component analysis Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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