Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19376
Title: On the dimensionality reduction for sparse representation based face recognition
Authors: Zhang, L 
Yang, M
Feng, Z
Zhang, D 
Issue Date: 2010
Source: Proceedings - International Conference on Pattern Recognition, 2010, p. 1237-1240 How to cite?
Abstract: Face recognition (FR) is an active yet challenging topic in computer vision applications. As a powerful tool to represent high dimensional data, recently sparse representation based classification (SRC) has been successfully used for FR. This paper discusses the dimensionality reduction (DR) of face images under the framework of SRC. Although one important merit of SRC is that it is insensitive to DR or feature extraction, a well trained projection matrix can lead to higher FR rate at a lower dimensionality. An SRC oriented unsupervised DR algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed DR algorithm over PCA or random projection based DR under the SRC framework.
Description: 2010 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, 23-26 August 2010
URI: http://hdl.handle.net/10397/19376
ISBN: 9780769541099
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.308
Appears in Collections:Conference Paper

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