Is face recognition really a compressive sensing problem?

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 553-560

  Shi, Qinfeng ^a   Eriksson, Anders ^a   Van Den Hengel, Anton ^a   Shen, Chunhua ^a,b  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b CSIRO   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSED SENSING;

COMPRESSIVE SENSING; DATA MEAN; SPARSE APPROXIMATIONS; STATE-OF-THE-ART APPROACH;

FACE RECOGNITION;

EID: 80052911858     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995556     Document Type: Conference Paper

References (19)

1. R. G. Baraniuk, M. Davenport, R. DeVore, and M. B. Wakin. A simple proof of the restricted isometry principle for random matrces. Constructive Approximation, 2007.
2. R. Basri and D. W. Jacobs. Lambertian refectance and linear subspaces. Patter Analysis and Machine Intelligence, IEEE Transactions on, 25(2):218-233, February 2003.
3. E. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: Exact signal reconstruction fom highly incomplete fequency information. IEEE Trns. Information Theor, 52(2):489-509, 2006.
4. E. Candes and T. Tao. Decoding by linear programming. IEEE Trns. Information Theor, 51(12):4203-4215, 2005.
5. E. J. Candes and M. B. Wakin. An introduction to compressive sensing. IEEE Signal Prcessing Magazine, pages 21-30, 2008.
6. Kuang chih Lee, Jefey Ho, and David Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trnsactions on Patter Analysis and Machine Intelligence, 27:684-698, 2005.
7. AS. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trns. Patter Anal. Mach. Intelligence, 23(6):643-660, 2001.
8. M. Grant and S. Boyd. Graph implementations for nonsmooth convex programs. In V. Blondel, S. Boyd, and H. Kimura, editors, Recent Advances in Learing and Control, Lecture Notes in Control and Information Sciences, pages 95-110. Springer-Verlag Limited, 2008. http://stanford.edu/-boyd/graph-dcp. html.
9. M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, version 1.21. http://cvxr.com/cvx, October 2010.
10. A Matinez and R. Benavente. The ar face database. Technical Report 24, CV C Tech. Report, 1998.
11. M. Rudelson and R. Veshynin. Geometric approach to eror correcting codes and reconstruction of signals. Int. Math. Res. Notices, 64:4019-4041, 2005.
12. Q. Shi, J. Petterson, G. Dror, J. Langford, A Smola, A Strehl, and S. V. N. Vishwanathan. Hash kerels. In Proc. Int. Workshop Artifcial Intell. & Statistics, 2009.
13. Q. Shi, J. Petterson, G. Dror, J. Langford, A J. Smola, and S. V. N. Vishwanathan. Hash kerels for structured data. J. Mach. Lear. Res., 10:2615-2637, 2009.
14. Qinfeng Shi, Hanxi Li, and Chunhua Shen. Rapid face recognition using hashing. In Proc. IEEE Con! Computer Vsion and Patter Recognition, San Francisco, USA, 2010.
15. J. A Tropp and A C. Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Information Teor, 53(12):4655-4666, 2007.
16. Robert J. Vanderbei. Linear Prgramming: Foundations and Extensions. 2nd, edition, 2008.
17. K. Weinberger, A Dasgupta, J. Attenberg, J. Langford, and AJ. Smola. Feature hashing for large scale multitask learning. In L. Bottou and M. Littman, editors, Proc. Int. Conf. Mach. Lear., 2009.
18. J. Wright, A Y. Yang, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. Patter Anal. Mach. Intelli., 2008.
19. A Y. Yang, J. Wright, Y. Ma, and S. S. Sastry. Feature selection in face recognition: A sparse representation perspective. Tech. Report, 2007.