Discriminative low-rank metric learning for face recognition

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015

Volumn , Issue , 2015, Pages

  Ding, Zhengming ^a   Suh, Sungjoo ^b   Han, Jae Joon ^b   Choi, Changkyu ^b   Fu, Yun ^a,c  

^a Northeastern University   (United States)

^b SAMSUNG Electronics   (South Korea)

^c Northeastern University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GESTURE RECOGNITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATRIX ALGEBRA;

DATA REPRESENTATIONS; FACE IDENTIFICATION; FACE VERIFICATION; LABELED FACES IN THE WILDS (LFW); LOW-DIMENSIONAL SUBSPACE; LOW-RANK REPRESENTATIONS; REDUNDANT FEATURES; VISUAL ANALYSIS;

FACE RECOGNITION;

EID: 84944929558     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FG.2015.7163088     Document Type: Conference Paper

References (30)

1. J. Hu, J. Lu, and Y.-P. Tan, "Discriminative deep metric learning for face verification in the wild," in IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1875-1882.
2. M. Kan, S. Shan, D. Xu, and X. Chen, "Side-information based linear discriminant analysis for face recognition." in British Machine Vision Conference, 2011, pp. 1-12.
3. Q. Cao, Y. Ying, and P. Li, "Similarity metric learning for face recognition," in IEEE International Conference on Computer Vision, 2013, pp. 2408-2415.
4. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, 2009.
5. E. P. Xing, M. I. Jordan, S. Russell, and A. Y. Ng, "Distance metric learning with application to clustering with side-information," in Neural information processing systems, 2002, pp. 505-512.
6. D. Lim, G. Lanckriet, and B. McFee, "Robust structural metric learning," in The 30th International Conference on Machine Learning, 2013, pp. 615-623.
7. A. Mignon and F. Jurie, "Pcca: A new approach for distance learning from sparse pairwise constraints," in IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2666-2672.
8. Z. Li, J. Liu, Y. Jiang, J. Tang, and H. Lu, "Low rank metric learning for social image retrieval," in The 20th ACM international conference on Multimedia, 2012, pp. 853-856.
9. G. Zhong, K. Huang, and C.-L. Liu, "Low rank metric learning with manifold regularization," in IEEE 11th International Conference on Data Mining, 2011, pp. 1266-1271.
10. G. Liu, Z. Lin, and Y. Yu, "Robust subspace segmentation by lowrank representation," in The 27th International Conference on Machine Learning, 2010, pp. 663-670.
11. G. Liu and S. Yan, "Latent low-rank representation for subspace segmentation and feature extraction," in IEEE International Conference on Computer Vision, 2011, pp. 1615-1622.
12. G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma, "Robust recovery of subspace structures by low-rank representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 171-184, 2013.
13. S. Li and Y. Fu, "Robust subspace discovery through supervised lowrank constraints," SIAM International Conference on Data Mining, 2014.
14. M. Shao, D. Kit, and Y. Fu, "Generalized transfer subspace learning through low-rank constraint," International Journal of Computer Vision, pp. 1-20, 2014.
15. Z. Ding, M. Shao, and Y. Fu, "Latent low-rank transfer subspace learning for missing modality recognition," in The 28th AAAI Conference on Artificial Intelligence, 2014.
16. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
17. B. Hariharan, J. Malik, and D. Ramanan, "Discriminative decorrelation for clustering and classification," in European Conference on Computer Vision, 2012, pp. 459-472.
18. M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
19. M. J. D. Powell, "A method for nonlinear constraints in minimization problems," Optimization, pp. 283-298, 1969.
20. M. R. Hestenes, "Multiplier and gradient methods," Journal of optimization theory and applications, vol. 4, no. 5, pp. 303-320, 1969.
21. D. Gabay and B. Mercier, "A dual algorithm for the solution of nonlinear variational problems via finite element approximation," Computers &Mathematics with Applications, vol. 2, no. 1, pp. 17-40, 1976.
22. J.-F. Cai, E. J. Candes, and Z. Shen, "A singular value thresholding algorithm for matrix completion," SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956-1982, 2010.
23. J. Yang, W. Yin, Y. Zhang, and Y. Wang, "A fast algorithm for edgepreserving variational multichannel image restoration," SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 569-592, 2009.
24. X. He and P. Niyogi, "Locality preserving projections," in Neural information processing systems, vol. 16, 2004, p. 153.
25. D. Cai, X. He, K. Zhou, J. Han, and H. Bao, "Locality sensitive discriminant analysis," in International Joint Conference on Artificial Intelligence, 2007.
26. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, "Labeled faces in the wild: A database for studying face recognition in unconstrained environments," University of Massachusetts, Amherst, Tech. Rep. 07-49, October 2007.
27. Y. Ying and P. Li, "Distance metric learning with eigenvalue optimization," The Journal of Machine Learning Research, vol. 13, no. 1, pp. 1-26, 2012.
28. J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, "Informationtheoretic metric learning," in The 24th International Conference on Machine Learning, 2007, pp. 209-216.
29. M. Guillaumin, J. Verbeek, and C. Schmid, "Is that you? metric learning approaches for face identification," in IEEE 12th International Conference on Computer Vision, 2009, pp. 498-505.
30. M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof, "Large scale metric learning from equivalence constraints," in IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2288-2295.