Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of three total variation based texture extraction models
Journal of Visual Communication and Image Representation
A Gabor Quotient Image for Face Recognition under Varying Illumination
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Fusion of classifiers for illumination robust face recognition
Expert Systems with Applications: An International Journal
Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition
IEICE - Transactions on Information and Systems
Retinex Combined with Total Variation for Image Illumination Normalization
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
An illumination normalization model for face recognition under varied lighting conditions
Pattern Recognition Letters
Illumination normalization for robust face recognition using discrete wavelet transform
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
A new method of illumination normalization for robust face recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
A new coarse-to-fine framework for 3d brain MR image registration
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
MQI based face recognition under uneven illumination
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L鹿 model: minimizing the total variation of the output cartoon while subject to anL鹿-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100% face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45% if not. These results, compared to the average 65% recognition rate of the quotient image model and the average 95% recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications - uneven background correction for cDNA microarray films and digital microscope images. We believe the proposed works can serve important roles in the related fields.