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Metaface learning for sparse representation based face recognition

11 years 3 months ago
Metaface learning for sparse representation based face recognition
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.
Meng Yang, Lei Zhang, Jian Yang, David Zhang
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICIP
Authors Meng Yang, Lei Zhang, Jian Yang, David Zhang
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