Nuclear-\(L_1\) Norm Joint Regression for Face Reconstruction and Recognition

  • Lei Luo
  • Jian YangEmail author
  • Jianjun Qian
  • Ying Tai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)


Recognizing a face with significant lighting, disguise and occlusion variations is an interesting and challenging problem in pattern recognition. To address this problem, many regression based methods, represented by sparse representation classifier (SRC), are presented recently. SRC uses the \(L_1\)-norm to characterize the pixel-level sparse noise but ignore the spatial information of noise. In this paper, we find that nuclear-norm is good for characterizing image-wise structural noise, and thus we use the nuclear norm and \(L_1\)-norm to jointly characterize the error image in regression model. Our experimental results demonstrate that the proposed method is more effective than state-of-the-art regression methods for face reconstruction and recognition.


Singular Value Decomposition Face Image Sparse Representation Structural Noise Nuclear Norm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nanjing University of Science and TechnologyNanjingPeople’s Republic of China

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