The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
On Photometric Issues in 3D Visual Recognition from aSingle 2D Image
International Journal of Computer Vision
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
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
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
Digital Image Processing
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
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)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Shadow compensation in 2D images for face recognition
Pattern Recognition
Learning from Real Images to Model Lighting Variations for Face Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Image and Vision Computing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Studies on hyperspectral face recognition in visible spectrum with feature band selection
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
Face illumination compensation dictionary
Neurocomputing
Separability oriented preprocessing for illumination-insensitive face recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
How does aging affect facial components?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
A comparative study on illumination preprocessing in face recognition
Pattern Recognition
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Illumination variation is one of intractable yet crucial problems in face recognition and many lighting normalization approaches have been proposed in the past decades. Nevertheless, most of them preprocess all the face images in the same way thus without considering the specific lighting in each face image. In this paper, we propose a lighting aware preprocessing (LAP) method, which performs adaptive preprocessing for each testing image according to its lighting attribute. Specifically, the lighting attribute of a testing face image is first estimated by using spherical harmonic model. Then, a von Mises-Fisher (vMF) distribution learnt from a training set is exploited to model the probability that the estimated lighting belongs to normal lighting. Based on this probability, adaptive preprocessing is performed to normalize the lighting variation in the input image. Extensive experiments on Extended YaleB and Multi-PIE face databases show the effectiveness of our proposed method.