Robust Face Recognition via Sparse Representation
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Allen Y. Yang | S. Shankar Sastry | John Wright | Yi Ma | Arvind Ganesh | S. Sastry | A. Yang | Arvind Ganesh | Yi Ma | John Wright | S. Sastry
[1] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[2] Thomas M. Cover,et al. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..
[3] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[4] H. Ellis,et al. Identification of Familiar and Unfamiliar Faces from Internal and External Features: Some Implications for Theories of Face Recognition , 1979, Perception.
[5] Alex Pentland,et al. Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[6] M. Turk,et al. Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.
[7] Joachim M. Buhmann,et al. Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.
[8] Pierre Comon,et al. Independent component analysis, A new concept? , 1994, Signal Process..
[9] Alex Pentland,et al. View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[10] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[11] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[12] David J. Kriegman,et al. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.
[13] Samuel Kaski,et al. Dimensionality reduction by random mapping: fast similarity computation for clustering , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[14] Aleix M. Martinez,et al. The AR face database , 1998 .
[15] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[16] A. Martínez,et al. The AR face databasae , 1998 .
[17] Edoardo Amaldi,et al. On the Approximability of Minimizing Nonzero Variables or Unsatisfied Relations in Linear Systems , 1998, Theor. Comput. Sci..
[18] Stan Z. Li,et al. Face recognition using the nearest feature line method , 1999, IEEE Trans. Neural Networks.
[19] David G. Stork,et al. Pattern Classification (2nd ed.) , 1999 .
[20] Horst Bischof,et al. Robust Recognition Using Eigenimages , 2000, Comput. Vis. Image Underst..
[21] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[22] Gregory Piatetsky-Shapiro,et al. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality , 2000 .
[23] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[24] Peter Bryant,et al. Model Selection using the Minimum Description Length Principle , 2000 .
[25] Heikki Mannila,et al. Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.
[26] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[27] 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..
[28] Bin Yu,et al. Model Selection and the Principle of Minimum Description Length , 2001 .
[29] Dimitris Achlioptas,et al. Database-friendly random projections , 2001, PODS.
[30] Stan Z. Li,et al. Learning spatially localized, parts-based representation , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[31] Aleix M. Martínez,et al. Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[32] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[33] Azriel Rosenfeld,et al. Face recognition: A literature survey , 2003, CSUR.
[34] P. Sinha,et al. The Role of Eyebrows in Face Recognition , 2003, Perception.
[35] David J. Kriegman,et al. Clustering appearances of objects under varying illumination conditions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[36] Ronen Basri,et al. Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[37] Haitao Wang,et al. Generalized quotient image , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[38] Tyng-Luh Liu,et al. Sparse Representations for Image Decompositions , 1999, International Journal of Computer Vision.
[39] Yuxiao Hu,et al. Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[40] I.B. Ciocoiu. Occluded face recognition using parts-based representation methods , 2005, Proceedings of the 2005 European Conference on Circuit Theory and Design, 2005..
[41] David L. Donoho,et al. Neighborly Polytopes And Sparse Solution Of Underdetermined Linear Equations , 2005 .
[42] E. Candès,et al. Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.
[43] David J. Kriegman,et al. Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] E. Candès,et al. Error correction via linear programming , 2005, FOCS 2005.
[45] E. Candes,et al. 11-magic : Recovery of sparse signals via convex programming , 2005 .
[46] Jongsun Kim,et al. Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[47] Sang Uk Lee,et al. Face recognition using face-ARG matching , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Chengjun Liu,et al. Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] D. Donoho,et al. Counting faces of randomly-projected polytopes when the projection radically lowers dimension , 2006, math/0607364.
[50] S. Park,et al. Partial & Holistic Face Recognition on FRGC-II data using Support Vector Machine , 2006, CVPR Workshops.
[51] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[52] D. Donoho. For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .
[53] Amnon Shashua,et al. Nonnegative Sparse PCA , 2006, NIPS.
[54] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[55] R. DeVore,et al. The Johnson-Lindenstrauss Lemma Meets Compressed Sensing , 2006 .
[56] Ke Huang,et al. Sparse Representation for Signal Classification , 2006, NIPS.
[57] E.J. Candes. Compressive Sampling , 2022 .
[58] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[59] Pawan Sinha,et al. Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.
[60] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[61] Tomaso Poggio,et al. Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines , 2006 .
[62] Matti Pietikäinen,et al. Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[63] Sanja Fidler,et al. Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[64] Michael I. Jordan,et al. A Direct Formulation for Sparse Pca Using Semidefinite Programming , 2004, SIAM Rev..
[65] Alice J. O'Toole,et al. FRVT 2006 and ICE 2006 large-scale results , 2007 .
[66] John Wright,et al. Computation and Relaxation of Conditions for Equivalence between ` 1 and ` 0 Minimization ∗ , 2007 .
[67] Allen Y. Yang,et al. Feature Selection in Face Recognition: A Sparse Representation Perspective , 2007 .
[68] Emmanuel J. Candès,et al. Highly Robust Error Correction byConvex Programming , 2006, IEEE Transactions on Information Theory.
[69] Yaakov Tsaig,et al. Fast Solution of $\ell _{1}$ -Norm Minimization Problems When the Solution May Be Sparse , 2008, IEEE Transactions on Information Theory.
[70] D. Donoho,et al. Fast Solution of -Norm Minimization Problems When the Solution May Be Sparse , 2008 .
[71] Pierre Vandergheynst,et al. Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.
[72] Richard G. Baraniuk,et al. Random Projections of Smooth Manifolds , 2009, Found. Comput. Math..
[73] R. Tibshirani,et al. A note on the group lasso and a sparse group lasso , 2010, 1001.0736.
[74] Alice J. O'Toole,et al. FRVT 2006 and ICE 2006 Large-Scale Experimental Results , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[75] O. Antoine,et al. Theory of Error-correcting Codes , 2022 .