Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D and 3D face recognition: A survey
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Relighting from a Single Image under Arbitrary Unknown Lighting Conditions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition under varying illumination using gradientfaces
IEEE Transactions on Image Processing
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
TILT: transform invariant low-rank textures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Robust principal component analysis?
Journal of the ACM (JACM)
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Comments on “Efficient and Robust Feature Extraction by Maximum Margin Criterion”
IEEE Transactions on Neural Networks
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Robust Recovery of Subspace Structures by Low-Rank Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
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In this paper, we consider the problem of recognizing human faces from frontal views with varying illumination, as well as occlusion and disguise. Motivated by the latest research on the recovery of low-rank matrix using robust principal component analysis (RPCA), we present a novel approach of robust face recognition by exploiting the sparse error component obtained by RPCA. Compared with low-rank component, it is revealed that the associated sparse error component exhibits more discriminating information which is of benefit to face identification. We define two descriptors (i.e., sparsity and smoothness) to represent characteristic of the sparse error component, and give two recognition protocols (i.e., the weighted based method and the ratio based method) to classify face images. The efficacy of the proposed approach is verified on publicly available databases (i.e., Extended Yale B and AR) with promising results. Meanwhile, the proposed algorithm manifests robustness since it does not assume any explicit prior knowledge about the illumination conditions, as well as the nature of corrupted and occluded regions. Furthermore, the proposed method is not limited to face recognition, also can be extended to other image-based object recognition.