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Author: search.filters.author.Rangsanseri, YuttapongSubject: search.filters.subject.Face recognition

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    Relevance-weighted (2D)2LDA image projection technique for face recognition
    (2009-08-01)
    Sanayha, Waiyawut
    ;
    Rangsanseri, Yuttapong
    In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or (2D)2LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted (2D)<sup>2</sup>LDA, or RW(2D) <sup>2</sup>LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW(2D)<sup>2</sup>LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%. © 2009.
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    Weighted LDA image projection technique for face recognition
    (2009-01-01)
    Sanayha, Waiyawut
    ;
    Rangsanseri, Yuttapong
    In this paper, we propose a novel image projection technique for face recognition applications based on Fisher Linear Discriminant Analysis (LDA). The projection is performed through a couple subspace analysis for overcoming the "small sample size" problem. Also, weighted pairwise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than that produced by the conventional LDA. The proposed technique has been successfully tested on three face databases. Experimental results indicate that the proposed algorithm outperforms the conventional algorithms. Copyright © 2009 The Institute of Electronics, Information and Communication Engineers.