Open AccessRelevance‐Weighted (2D) 2 LDA Image Projection Technique for Face Recognition
Author(s) -
Sanayha Waiyawut,
Rangsanseri Yuttapong
Publication year - 2009
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.09.0108.0667
Subject(s) - linear discriminant analysis , pattern recognition (psychology) , facial recognition system , artificial intelligence , projection (relational algebra) , scatter matrix , hyperplane , mathematics , face (sociological concept) , discriminant , matrix (chemical analysis) , computer science , algorithm , covariance matrix , social science , materials science , geometry , sociology , estimation of covariance matrices , composite material
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) 2 LDA, 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) 2 LDA, or RW(2D) 2 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) 2 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%.