Total variation models for variable lighting face recognition

By Terrence Chen, Wotao Yin, Xiang Sean Zhou, Dorin Comaniciu, Thomas S. Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

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Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model (2010, result): “This lighting distribution is independent with the current lighting setting, and according to Lambertian model, it should go into the albedo part but (depending on the setting of λ ) the LTV decomposition (c.f ., (6)) often allow a significant amount of fine scene texture to leak into µ, thus making”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, background): “The ﬁrst deﬁned LBP’s for neighborhoods of different sizes, thus making it feasible to deal with textures at different scales.”
Face Recognition Under Varying Illumination Using Gradientfaces (2009, methodology): “mination fields, in order to overcome this limitation, the authors in [23]”
An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise (2009, unknown): “These results have led to the applications of the TVL1 model in medical imaging [53, 15] and computer vision [17, 16], in which this model has shown its advantages over some competing models because it requires no feature or structural information beside spacial scales.”