Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Normalization for Robust Face Recognition Against Varying Lighting Conditions
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition under occlusions and variant expressions with partial similarity
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Image Processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
Gabor feature based sparse representation for face recognition with gabor occlusion dictionary
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression for face recognition
Pattern Recognition
Robust sparse coding for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Size of the dictionary in matching pursuit algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Sparse Representation for Color Image Restoration
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Joint Learning for Single-Image Super-Resolution via a Coupled Constraint
IEEE Transactions on Image Processing
Fisher Discrimination Dictionary Learning for sparse representation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Partial face recognition: An alignment free approach
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.01 |
Sparse representation based classification (SRC) has recently been proposed for robust face recognition. To deal with occlusion, SRC introduces an identity matrix as an occlusion dictionary on the assumption that the occlusion has sparse representation in this dictionary. However, the results show that SRC's use of this occlusion dictionary is not nearly as robust to large occlusion as it is to random pixel corruption. In addition, the identity matrix renders the expanded dictionary large, which results in expensive computation. In this paper, we present a novel method, namely structured sparse representation based classification (SSRC), for face recognition with occlusion. A novel structured dictionary learning method is proposed to learn an occlusion dictionary from the data instead of an identity matrix. Specifically, a mutual incoherence of dictionaries regularization term is incorporated into the dictionary learning objective function which encourages the occlusion dictionary to be as independent as possible of the training sample dictionary. So that the occlusion can then be sparsely represented by the linear combination of the atoms from the learned occlusion dictionary and effectively separated from the occluded face image. The classification can thus be efficiently carried out on the recovered non-occluded face images and the size of the expanded dictionary is also much smaller than that used in SRC. The extensive experiments demonstrate that the proposed method achieves better results than the existing sparse representation based face recognition methods, especially in dealing with large region contiguous occlusion and severe illumination variation, while the computational cost is much lower.