Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15688
Title: Laplacian bidirectional PCA for face recognition
Authors: Yang, W
Sun, C
Zhang, L 
Ricanek, K
Keywords: 2DPCA
BDPCA
Face recognition
Laplacian
Issue Date: 2010
Publisher: Elsevier
Source: Neurocomputing, 2010, v. 74, no. 1-3, p. 487-493 How to cite?
Journal: Neurocomputing 
Abstract: Two-dimensional principal components analysis (2DPCA) needs more coefficients than principal components analysis (PCA) for image representation and hence needs more time for classification. The bidirectional PCA (BDPCA) is proposed to overcome these drawbacks of 2DPCA. Both 2DPCA and BDPCA, however, can work only in Euclidean space. In this paper, we propose Laplacian BDPCA (LBDPCA) to enhance the robustness of BDPCA by extending it to non-Euclidean space. Experimental results on representative face databases show that LBDPCA works well and it surpasses BDPCA.
URI: http://hdl.handle.net/10397/15688
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2010.08.020
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

16
Last Week
0
Last month
0
Citations as of Sep 8, 2017

WEB OF SCIENCETM
Citations

13
Last Week
0
Last month
0
Citations as of Sep 5, 2017

Page view(s)

48
Last Week
2
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.