Letters: Enhancing sparsity via ℓp (0

Sparse representation has received an increasing amount of interest in recent years. By representing the testing image as a sparse linear combination of the training samples, sparse representation based classification (SRC) has been successfully applied in face recognition. In SRC, the @?^1 minimization instead of the @?^0 minimization is used to seek for the sparse solution for its computational convenience and efficiency. However, @?^1 minimization does not always yield sufficiently sparse solution in many practical applications. In this paper, we propose a novel SRC method, namely the @?^p (0

[1]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Wei Liang,et al.  Similarity-based image classification via kernelized sparse representation , 2010, 2010 IEEE International Conference on Image Processing.

[3]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[6]  Allen Y. Yang,et al.  Fast ℓ1-minimization algorithms and an application in robust face recognition: A review , 2010, 2010 IEEE International Conference on Image Processing.

[7]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[8]  S. Foucart,et al.  Sparsest solutions of underdetermined linear systems via ℓq-minimization for 0 , 2009 .

[9]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[10]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Chun Chen,et al.  Graph Regularized Sparse Coding for Image Representation , 2011, IEEE Transactions on Image Processing.

[13]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[14]  R. Chartrand,et al.  Restricted isometry properties and nonconvex compressive sensing , 2007 .

[15]  Ran He,et al.  Maximum Correntropy Criterion for Robust Face Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Ke Huang,et al.  Sparse Representation for Signal Classification , 2006, NIPS.

[17]  Wotao Yin,et al.  Iteratively reweighted algorithms for compressive sensing , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[18]  Edoardo Amaldi,et al.  On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..

[19]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[20]  Rick Chartrand,et al.  Exact Reconstruction of Sparse Signals via Nonconvex Minimization , 2007, IEEE Signal Processing Letters.

[21]  Yueting Zhuang,et al.  Sparse representation using nonnegative curds and whey , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Ran He,et al.  A Regularized Correntropy Framework for Robust Pattern Recognition , 2011, Neural Computation.

[23]  E. Candes,et al.  11-magic : Recovery of sparse signals via convex programming , 2005 .

[24]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[25]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[27]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[28]  Wang Yao,et al.  L 1/2 regularization , 2010 .

[29]  D. Donoho.  For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[30]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[31]  Tomaso Poggio,et al.  Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines , 2006 .

[32]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.