Paper Info
Title
Full-Space LDA With Evolutionary Selection for Face Recognition
Abstract
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
Year
DOI
Venue
2006
null
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
DocType
Volume
lda method,face recognition,orl face image database,estimation of distribution algorithm,discriminative information,matrix algebra,full-space linear discriminant analysis,full advantage,feature extraction,high dimensional face data,eigenvectors,full space,scatter matrix,range subspaces,eigenvalues and eigenfunctions,evolutionary selection,full-space lda
Conference
4456 LNAI
Issue
ISSN
ISBN
null
16113349
1-4244-0605-6
Citations 
PageRank 
References 
1
0.36
8
Authors
5
Name
Order
Citations
PageRank
Xin Li162.57
Bin Li278279.80
Chen Hong32111.66
Xian-ji Wang4452.41
Zhengquan Zhuang510.36