Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15911
Title: A two-phase test sample sparse representation method for use with face recognition
Authors: Xu, Y
Zhang, D 
Yang, J
Yang, JY
Keywords: Computer vision
Face recognition
Pattern recognition
Sparse representation
Transform methods
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on circuits and systems for video technology, 2011, v. 21, no. 9, 5742988, p. 1255-1262 How to cite?
Journal: IEEE transactions on circuits and systems for video technology 
Abstract: In this paper, we propose a two-phase test sample representation method for face recognition. The first phase of the proposed method seeks to represent the test sample as a linear combination of all the training samples and exploits the representation ability of each training sample to determine M nearest neighbors for the test sample. The second phase represents the test sample as a linear combination of the determined M nearest neighbors and uses the representation result to perform classification. We propose this method with the following assumption: the test sample and its some neighbors are probably from the same class. Thus, we use the first phase to detect the training samples that are far from the test sample and assume that these samples have no effects on the ultimate classification decision. This is helpful to accurately classify the test sample. We will also show the probability explanation of the proposed method. A number of face recognition experiments show that our method performs very well.
URI: http://hdl.handle.net/10397/15911
ISSN: 1051-8215
EISSN: 1558-2205
DOI: 10.1109/TCSVT.2011.2138790
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

244
Last Week
1
Last month
9
Citations as of Aug 13, 2017

WEB OF SCIENCETM
Citations

203
Last Week
0
Last month
7
Citations as of Aug 20, 2017

Page view(s)

55
Last Week
3
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



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