Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Geometry and photometry in three-dimensional visual recognition
Geometry and photometry in three-dimensional visual recognition
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
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
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
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
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
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face Recognition Using the Classified Appearance-based Quotient Image
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient illumination normalization method for face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Robust face recognition using 2D and 3D data: Pose and illumination compensation
Pattern Recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
An integrated framework for biometrics security
iUBICOM'10 Proceedings of the 5th international conference on Ubiquitous and Collaborative Computing
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In this paper, a novel illumination normalization model is proposed for the pre-processing of face recognition under varied lighting conditions. The novel model could compensate all the illumination effects in face samples, like the diffuse reflection, specular reflection, attached shadow and cast shadow. Firstly, it uses the TV_L^1 model to get the low-frequency part of face image, and adopts the self-quotient model to normalize the diffuse reflection and attached shadow. Then it generates the illumination invariant small-scale part of face sample. Secondly, TV_L^2 model is used to get the noiseless large-scale part of face sample. All kinds of illumination effects in the large-scale part are further removed by the region-based histogram equalization. Thirdly, two parts are fused to generate the illumination invariant face sample. The result of our model contains multi-scaled image information, and all illumination effects in face samples are compensated. Finally, high-order statistical relationships among variables of samples are extracted for classifier. Experimental results on some large scale face databases prove that the processed image by our model could largely improve the recognition performances of conventional methods under low-level lighting conditions.