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The Scientific World Journal
Volume 2014, Article ID 519158, 10 pages
http://dx.doi.org/10.1155/2014/519158
Research Article

Efficient Detection of Occlusion prior to Robust Face Recognition

1Department of Multimedia Communications, EURECOM, 450 Route des Chappes, 06410 Biot, France
2Center for Machine Vision Research, Department of Computer Science and Engineering, University of Oulu, P.O. Box 4500, 90014 Oulu, Finland

Received 26 August 2013; Accepted 7 October 2013; Published 16 January 2014

Academic Editors: S. Berretti, S. Hong, and T. Yamasaki

Copyright © 2014 Rui Min 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. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '91), pp. 586–591, June 1991. View at Scopus
  3. 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. View at Publisher · View at Google Scholar · View at Scopus
  4. T. Ahonen, A. Hadid, and M. Pietikäinen, “Face description with local binary patterns: application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. P. S. Penev and J. J. Atick, “Local feature analysis: a general statistical theory for object representation,” Network, vol. 7, no. 3, pp. 477–500, 1996. View at Google Scholar · View at Scopus
  6. A. M. Martínez, “Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 748–763, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Tan, S. Chen, Z.-H. Zhou, and F. Zhang, “Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft κ-NN ensemble,” IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 875–886, 2005. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Kim, J. Choi, J. Yi, and M. Turk, “Effective representation using ICA for face recognition robust to local distortion and partial occlusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1977–1981, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Fidler, D. Skočaj, and A. Leonardis, “Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 337–350, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. B.-G. Park, K.-M. Lee, and S.-U. Lee, “Face recognition using face-ARG matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 1982–1988, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): a novel non-statistical model for face representation and recognition,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), pp. 786–791, IEEE Computer Society, Washington, DC, USA, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Jia and A. M. Martinez, “Support vector machines in face recognition with occlusions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 136–141, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. 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
  14. Z. Zhou, A. Wagner, H. Mobahi, J. Wright, and Y. Ma, “Face recognition with contiguous occlusion using Markov Random Fields,” in Proceedings of the 12th International Conference on Computer Vision (ICCV '09), pp. 1050–1057, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Yang and L. Zhang, “Gabor feature based sparse representation for face recognition with gabor occlusion dictionary,” in Proceedings of the 11th European conference on Computer vision (ECCV '10), pp. 448–461, Springer, Berlin, Germany, 2010.
  16. S. Liao and A. K. Jain, “Partial face recognition: an alignment free approach,” in Proceedings of the International Joint Conference on Biometrics (IJCB '11), October 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Yang, L. Zhang, J. Yang, and D. Zhang, “Robust sparse coding for face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 625–632, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Rama, F. Tarres, L. Goldmann, and T. Sikora, “More robust face recognition by considering occlusion information,” in Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG '08), September 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. H. J. Oh, K. M. Lee, and S. U. Lee, “Occlusion invariant face recognition using selective local non-negative matrix factorization basis images,” Image and Vision Computing, vol. 26, no. 11, pp. 1515–1523, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. W. Zhang, S. Shan, X. Chen, and W. Gao, “Local Gabor binary patterns based on Kullback-Leibler divergence for partially occluded face recognition,” IEEE Signal Processing Letters, vol. 14, no. 11, pp. 875–878, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Min, A. Hadid, and J.-L. Dugelay, “Improving the recognition of faces occluded by facial accessories,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG '11), pp. 442–447, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Boykov, O. Veksler, and R. Zabih, “Markov random fields with efficient approximations,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 648–655, June 1998. View at Publisher · View at Google Scholar · View at Scopus
  23. A. M. Martinez, “The AR face database,” CVC Technical Report 24, 1998. View at Google Scholar
  24. D. L. Donoho, “High-dimensional data analysis: the curses and blessings of dimensionality,” in Proceedings of the American Mathematical Society Conference Math Challenges of the 21st Century, 2000.
  25. S. Z. Li, X. W. Hou, H. J. Zhang, and Q. S. Cheng, “Learning spatially localized, parts-based representation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I207–I212, December 2001. View at Scopus
  26. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  27. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar
  28. S. Geman and D. Geman, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721–741, 1984. View at Google Scholar · View at Scopus
  29. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222–1239, 2001. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124–1137, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts?” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 147–159, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. P. Sinha, B. Balas, Y. Ostrovsky, and R. Russell, “Face recognition by humans: nineteen results all computer vision researchers should know about,” Proceedings of the IEEE, vol. 94, no. 11, pp. 1948–1961, 2006. View at Publisher · View at Google Scholar · View at Scopus