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On the Dimensionality Reduction for Sparse Representation Based Face Recognition

11 years 6 months ago
On the Dimensionality Reduction 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 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.
Lei Zhang, Meng Yang, Zhizhao Feng, David Zhang
Added 20 Jul 2010
Updated 20 Jul 2010
Type Conference
Year 2010
Where ICPR
Authors Lei Zhang, Meng Yang, Zhizhao Feng, David Zhang
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