Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
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
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
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
Journal of Cognitive Neuroscience
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
Lighting aware preprocessing for face recognition across varying illumination
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
Face illumination transfer through edge-preserving filters
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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Locally Linear Regression for Pose-Invariant Face Recognition
IEEE Transactions on Image Processing
Normalization of Face Illumination Based on Large-and Small-Scale Features
IEEE Transactions on Image Processing
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Face images captured under distinct lighting conditions have totally different overall appearances, which greatly degrade the recognition accuracy. In this paper, an illumination compensation strategy is worked out to assist linear representation based face recognition. In the past few years, linear representation based face recognition approaches such as SRC and CRC_RLS attract great attention, but their effectiveness greatly depends on a large number of training samples, which seriously restricts their application values. We will illustrate that face illumination distinction could be compensated just through a general linear dictionary, and after enrolling our illumination compensation strategy, even there is only single gallery image for each subject, linear representation recognition approaches can still be relatively robust to probe illumination variance. The proposed strategy is experimented on the Extended Yale B and CMU PIE database.