Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22248
Title: Sequential row-column independent component analysis for face recognition
Authors: Gao, Q
Zhang, L 
Zhang, D 
Keywords: Face recognition
Feature extraction
Independent component analysis (ICA)
Issue Date: 2009
Publisher: Elsevier
Source: Neurocomputing, 2009, v. 72, no. 4-6, p. 1152-1159 How to cite?
Journal: Neurocomputing 
Abstract: This paper presents a novel subspace method called sequential row-column independent component analysis (RC-ICA) for face recognition. Unlike the traditional ICA, in which the face image is transformed into a vector before calculating the independent components (ICs), RC-ICA consists of two sequential stages-an image row-ICA followed by a column-ICA. There is no image-to-vector transformation in both the stages and the ICs are computed directly in the subspace spanned by the row or column vectors. RC-ICA can reduce the face recognition error caused by the dilemma in traditional ICA, i.e. the number of available training samples is greatly less than that of the dimension of training vector. Another advantage of RC-ICA over traditional ICA is that the dimensionality of the recognition subspace is much smaller, which means that the face image can have a more condensed representation. Extensive experiments are performed on the well-known Yale-B, AR and FERET databases to validate the proposed method and the experimental results show that the RC-ICA achieves higher recognition accuracy than ICA and other existing subspace methods while using a subspace of smaller dimensionality.
URI: http://hdl.handle.net/10397/22248
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2008.02.007
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

21
Last Week
0
Last month
0
Citations as of Oct 9, 2017

WEB OF SCIENCETM
Citations

17
Last Week
0
Last month
0
Citations as of Oct 15, 2017

Page view(s)

45
Last Week
0
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



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