Mining discriminative components with low-rank and sparsity constraints for face recognition

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 1469-1477

  Zhang, Qiang ^a   Li, Baoxin ^a  

^a Arizona State University   (United States)

Author keywords

component decomposition; face recognition; low rank matrix; sparse matrix; subspace learning

Indexed keywords

COMPONENT DECOMPOSITION; DISCRIMINATIVE COMPONENTS; FACE DATA; FACE IMAGES; GLOBAL CONDITIONS; ILLUMINATION CONDITIONS; IMAGE DECOMPOSITION; INPUT IMAGE; LOW-RANK MATRICES; ORIGINAL IMAGES; SPARSE MATRICES; SPARSITY CONSTRAINTS; SUBSPACE LEARNING; THREE COMPONENT;

DATA MINING; EXPERIMENTS;

FACE RECOGNITION;

EID: 84866020210     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339760     Document Type: Conference Paper

References (19)

1. M. Aharon, M. Elad, and A. Bruckstein. K-SVD: Design of dictionaries for sparse representation. Proceedings of SPARS, 5, 2005.
2. J. F. Cai, E. J. Candes, and Z. Shen. A singular value thresholding algorithm for matrix completion. preprint, 2008.
3. E. Candes and Y. Plan. Matrix completion with noise. Proceedings of the IEEE, 2009.
4. A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):643-660, 2001.
5. D. Goldfarb and S. Ma. Convergence of fixed-point continuation algorithms for matrix rank minimization. Foundations of Computational Mathematics, pages 1-28, 2011.
6. 1-minimization: Methodology and convergence. SIAM Journal on Optimization, 19(3):1107-1130, 2008.
7. C. Kanan and G. Cottrell. Robust classification of objects, faces, and flowers using natural image statistics. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 2472-2479, june 2010.
8. A. Knyazev and M. Argentati. Principal angles between subspaces in an A-based scalar product: algorithms and perturbation estimates. SIAM Journal on Scientific Computing, 23(6):2008-2040, 2002.
9. R. Kumar, A. Banerjee, and B. Vemuri. Volterrafaces: Discriminant analysis using volterra kernels. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 150-155, june 2009.
10. K. Lee, J. Ho, and D. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 684-698, 2005.
11. Z. Lin, M. Chen, L. Wu, and Y. Ma. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Arxiv preprint arXiv:1009.5055, 2010.
12. J. Liu, S. Chen, and X. Tan. Fractional order singular value decomposition representation for face recognition. Pattern Recogn., 41:378-395, January 2008.
13. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE, 2008.
14. A. Martinez and R. Benavente. The AR face database. Technical report, CVC Technical report, 1998.
15. P. Nagesh and B. Li. A compressive sensing approach for expression-invariant face recognition. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1518-1525, 2009.
16. J. Portilla and E. Simoncelli. A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision, 40(1):49-70, 2000.
17. T. Sim, S. Baker, and M. Bsat. The CMU pose, illumination, and expression (PIE) database. In Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition, 2002.
18. J. Wright, A. Ganesh, S. Rao, Y. Peng, and Y. Ma. Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization. In Advances in Neural Information Processing Systems 22.
19. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2):210-227, 2008.