Joint dynamic sparse representation for multi-view face recognition
Pattern Recognition
Spatio-temporal video representation with locality-constrained linear coding
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Weighted group sparse representation based on robust regression for face recognition
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Kernel sparse locality preserving canonical correlation analysis for multi-modal feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Sparse coding based visual tracking: Review and experimental comparison
Pattern Recognition
An improved fisher discriminant dictionary learning for video object tracking
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
With one look: robust face recognition using single sample per person
Proceedings of the 21st ACM international conference on Multimedia
Robust face recognition via occlusion dictionary learning
Pattern Recognition
Linear reconstruction measure steered nearest neighbor classification framework
Pattern Recognition
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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 l_2-norm or l_1-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.