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- W827142139 abstract "Occlusion, real disguise and illumination are still the common difficulties encountered in face recognition. The sparse representation based classifier (SRC) has shown a great potential in handling pixel-level sparse noise, while the nuclear norm based matrix regression (NMR) model has been demonstrated to be powerful for dealing with the image-wise structural noise. Both methods, however, might be not very effective for handling the mixed noise: the structural noise plus the sparse noise. In this paper, we present two nuclear-L1 norm joint matrix regression (NL1R) models for face recognition with mixed noise, which are derived by using MAP (maximum a posteriori probability estimation). The first model considers the mixed noise as a whole, while the second model assumes the mixed noise is an additive combination of two independent componenral nts: sparse noise and structuoise. The proposed models can be solved by the alternating direction method of multipliers (ADMM). We validate the effectiveness of the proposed models through a series of experiments on face reconstruction and recognition." @default.
- W827142139 created "2016-06-24" @default.
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- W827142139 date "2015-12-01" @default.
- W827142139 modified "2023-10-17" @default.
- W827142139 title "Nuclear-L1 norm joint regression for face reconstruction and recognition with mixed noise" @default.
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- W827142139 doi "https://doi.org/10.1016/j.patcog.2015.06.012" @default.
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