Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1215
Title: Block independent component analysis for face recognition
Authors: Zhang, L 
Gao, Q
Zhang, DD 
Keywords: Face recognition
Hemodynamics
Image analysis
Multivariant analysis
Vectors
Issue Date: 2007
Publisher: IEEE Computer Society
Source: 14th International Conference on Image Analysis and Processing : Modena, Italy, September 10-14, 2007 : proceedings, p. 217-222 How to cite?
Abstract: This paper presents a subspace algorithm called block independent component analysis (B-ICA) for face recognition. Unlike the traditional ICA, in which the whole face image is stretched into a vector before calculating the independent components (ICs), B-ICA partitions the facial images into blocks and takes the block as the training vector. Since the dimensionality of the training vector in B-ICA is much smaller than that in traditional ICA, it can reduce the face recognition error caused by the dilemma in ICA, i.e. the number of available training samples is greatly less than that of the dimension of training vector. Experiments on the well-known Yale and AR databases validate that the B-ICA can achieve higher recognition accuracy than ICA and enhanced ICA (EICA).
URI: http://hdl.handle.net/10397/1215
ISBN: 0-7695-2877-5
9780769528779
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
face-recognition_07.pdf345.37 kBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
0
Citations as of Apr 19, 2016

Page view(s)

1,349
Last Week
0
Last month
Checked on Sep 25, 2016

Download(s)

527
Checked on Sep 25, 2016

Google ScholarTM

Check



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