Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33440
Title: Image set-based collaborative representation for face recognition
Authors: Zhu, P
Zuo, W
Zhang, L 
Shiu, SCK 
Zhang, D 
Keywords: Collaborative representation
Face recognition
Image set
Set to sets distance
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information forensics and security, 2014, v. 9, no. 7, 6816042, p. 1120-1132 How to cite?
Journal: IEEE transactions on information forensics and security 
Abstract: With the rapid development of digital imaging and communication technologies, image set-based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set using the gallery face image sets. The set-to-set distance-based methods ignore the relationship between gallery sets, whereas representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set-based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image-based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.
URI: http://hdl.handle.net/10397/33440
ISSN: 1556-6013 (print)
1556-6021 (online)
DOI: 10.1109/TIFS.2014.2324277
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

32
Last Week
0
Last month
0
Citations as of May 20, 2017

WEB OF SCIENCETM
Citations

35
Last Week
1
Last month
0
Citations as of May 23, 2017

Page view(s)

40
Last Week
2
Last month
Checked on May 21, 2017

Google ScholarTM

Check

Altmetric



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