Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 597245, 10 pages
http://dx.doi.org/10.1155/2014/597245
Research Article

Robust Tensor Preserving Projection for Multispectral Face Recognition

1Automation Department, Donghua University, Shanghai 201620, China
2Automation Department, East China University of Science and Technology, Shanghai 200237, China
3School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China

Received 14 April 2014; Accepted 21 July 2014; Published 12 August 2014

Academic Editor: Haipeng Peng

Copyright © 2014 Shaoyuan Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. G. Kong, J. Heo, F. Boughorbel et al., “Multiscale fusion of visible and thermal IR images for illumination-invariant face recognition,” International Journal of Computer Vision, vol. 71, no. 2, pp. 215–233, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. S. G. Kong, J. Heo, B. R. Abidi, J. Paik, and M. A. Abidi, “Recent advances in visual and infrared face recognition—a review,” Computer Vision and Image Understanding, vol. 97, no. 1, pp. 103–135, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. D. A. Socolinsky and A. Selinger, “A comparative analysis of face recognition performance with visible and thermal infrared imagery,” in Proceedings of the 16th International Conference on Pattern Recognition, pp. 217–222, IEEE, 2002.
  4. Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Face recognition in hyperspectral images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1552–1560, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Pan, G. Healey, M. Prasad, and B. Tromberg, “Illumination-invariant face recognition in hyperspectral images,” in Proceedings of SPIE: Algorithms and Technologies for Multispectral, Hyperspectral , and Ultraspectral Imagery IX, vol. 5093, pp. 275–282, 2003.
  6. L. Denes, P. Metes, and Y. Liu, “Hyperspectral face database,” Tech. Rep. CMU-RI-TR-02-25, 2002. View at Google Scholar
  7. Y. Chou and P. Bajcsy, “Toward face detection, pose estimation and human recognition from hyperspectral imagery,” Tech. Rep. NCSA-ALG-04-0005, 2004, http://isda.ncsa.uiuc.edu/peter/. View at Google Scholar
  8. S. Wang, Z. Liu, S. Lv et al., “A natural visible and infrared facial expression database for expression recognition and emotion inference,” IEEE Transactions on Multimedia, vol. 12, no. 7, pp. 682–691, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Di, L. Zhang, D. Zhang, and Q. Pan, “Studies on hyperspectral face recognition in visible spectrum with feature band selection,” IEEE Transactions on Systems, Man, and Cybernetics A: Systems and Humans, vol. 40, no. 6, pp. 1354–1361, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Selinger and D. A. Socolinsky, “Appearance-based facial recognition using visible and thermal imagery: a comparative study,” Tech. Rep., Equinox Corporation, 2000. View at Google Scholar
  11. X. Chen, P. Flynn, and K. Bowyer, “PCA-based face recognition in infrared imagery: baseline and comparative studies,” in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003.
  12. S. Wang, J. Yang, M. Sun, X. Peng, M. Sun, and C. Zhou, “Sparse tensor discriminant color space for face verification,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 6, pp. 876–888, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Singh, A. Gyaourova, G. Bebis, and I. Pavlidis, “Infrared and visible image fusion for face recognition,” in Biometric Technology for Human Identification, vol. 5404 of Proceedings of the SPIE, pp. 585–596, April 2004. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Heo, S. G. Kong, B. R. Abidi, and M. A. Abidi, “Fusion of visual and thermal signatures with eyeglass removal for robust face recognition,” in Proceedings of IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum, pp. 94–99, 2004.
  15. O. Arandjelovic, “Multi-sensory face biometric fusion (for personal identification) method details,” in Proceedings of IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum, pp. 1–8, 2006.
  16. H. Chang, H. Harishwaran, M. Yi, A. Koschan, B. Abidi, and M. Abidi, “An indoor and outdoor, multimodal, multispectral and multi-illuminant database for face recognition,” in Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop (CVPRW '06), p. 54, June 2006. View at Publisher · View at Google Scholar
  17. W. K. Wong and H. Zhao, “Eyeglasses removal of thermal image based on visible information,” Information Fusion, vol. 14, no. 2, pp. 163–176, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, “Face recognition using Laplacianfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328–340, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Dai and D. Y. Yeung, “Tensor embedding methods,” in Proceedings of the 21st AAAI Conference on Artificial Intelligence, vol. 1, pp. 330–335, 2005.
  20. D. Zheng, X. Du, and L. Cui, “Tensor locality preserving projections for face recognition,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '10), vol. 1, pp. 2347–2350, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “MPCA: multilinear principal component analysis of tensor objects,” IEEE Transactions on Neural Networks, vol. 19, no. 1, pp. 18–39, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Lu, K. N. K. Plataniotis, and A. N. Venetsanopoulos, “Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning,” IEEE Transactions on Neural Networks, vol. 20, no. 11, pp. 1820–1836, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Xu, S. Yan, L. Zhang, S. Lin, H. Zhang, and T. S. Huang, “Reconstruction and recognition of tensor-based objects with concurrent subspaces analysis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 1, pp. 36–47, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Zhou, D. Tao, and X. Wu, “Manifold elastic net: a unified framework for sparse dimension reduction,” Data Mining and Knowledge Discovery, vol. 22, no. 3, pp. 340–371, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. 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. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Qiao, S. Chen, and X. Tan, “Sparsity preserving projections with applications to face recognition,” Pattern Recognition, vol. 43, no. 1, pp. 331–341, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  27. B. Cheng, J. Yang, S. Yan, Y. Fu, and T. S. Huang, “Learning with l1-graph for image analysis,” IEEE Transactions on Image Processing, vol. 19, no. 4, pp. 858–866, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. H. Moon and P. Phillips, “The FERET verification testing protocol for face recognition algorithms,” in Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 48–53, 1998.
  29. B. Bader and T. G. Kolda, Tensor toolbox version 2.3, Sandia National Laboratories, 2009, http://www.sandia.gov/~tgkolda/TensorToolbox/index-2.3.html.
  30. J. Friedman, T. Hastie, and R. Tibshirani, “Regularization paths for generalized linear models via coordinate descent,” Journal of Statistical Software, vol. 33, no. 1, pp. 1–22, 2010. View at Google Scholar · View at Scopus
  31. M. Rosen and X. Jiang, “Lippmann2000: a spectral image database under construction,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, pp. 117–122, 1999.
  32. T. Igarashi, K. Nishino, and S. Nayar, “The appearance of human skin,” Tech. Rep. CUCS-02405, Columbia University, 2005. View at Google Scholar