Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15500
Title: Metaface learning for sparse representation based face recognition
Authors: Yang, M
Zhang, L 
Yang, J
Zhang, D 
Keywords: Face recognition
Metaface learning
Sparse representation
Issue Date: 2010
Source: Proceedings - International Conference on Image Processing, ICIP, 2010, p. 1601-1604 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 metaface learning (MFL) of face images under the framework of SRC. Although directly using the training samples as dictionary bases can achieve good FR performance, a well learned dictionary matrix can lead to higher FR rate with less dictionary atoms. An SRC oriented unsupervised MFL algorithm is proposed in this paper and the experimental results on benchmark face databases demonstrated the improvements brought by the proposed MFL algorithm over original SRC.
Description: 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010
URI: http://hdl.handle.net/10397/15500
ISBN: 9781424479948
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5652363
Appears in Collections:Conference Paper

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