Abstract
Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity-constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to outliers (e.g., occlusions, corruptions, etc.) than SRC. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive experiments on representative face databases demonstrate that the RSC scheme is much more effective than state-of-the-art methods in dealing with face occlusion, corruption, lighting and expression changes, etc.
Original language | English |
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Title of host publication | 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 |
Pages | 625-632 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 22 Sept 2011 |
Event | 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 - Colorado Springs, CO, United States Duration: 20 Jun 2011 → 25 Jun 2011 |
Conference
Conference | 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011 |
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Country/Territory | United States |
City | Colorado Springs, CO |
Period | 20/06/11 → 25/06/11 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition