Robust regression and outlier detection
Robust regression and outlier detection
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust sparse coding for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust classification using structured sparse representation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
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Recently, sparse representations have attracted a lot of attention. In this paper, we present a novel group sparse representation based on robust regression approach (GSRR) by modeling the sparse coding as group sparse constrained robust regression problem. Unlike traditional group sparse representation, we propose a weighted group sparse penalty which integrates similarity between the test sample and distinct classes and data locality. An efficient iteratively reweighted sparse coding algorithm is proposed to solve the GSRR model. The proposed classification algorithm has been evaluated on three publicly available face databases under varying illuminations and poses. The experimental results demonstrate that the performance of our algorithm is better than that of the state of the art methods.