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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4579398, 11 pages
https://doi.org/10.1155/2017/4579398
Research Article

A Robust Shape Reconstruction Method for Facial Feature Point Detection

School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China

Correspondence should be addressed to Shuqiu Tan; moc.liamtoh@631321uiquhsnat and Dongyi Chen; nc.ude.ctseu@nehcyd

Received 24 October 2016; Revised 18 January 2017; Accepted 30 January 2017; Published 19 February 2017

Academic Editor: Ezequiel López-Rubio

Copyright © 2017 Shuqiu Tan 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. Z. Zhang, P. Luo, C. Change Loy, and X. Tang, “Facial landmark detection by deep multi-task learning,” in Proceedings of the European Conference on Computer Vision (ECCV '14), pp. 94–108, Springer International, Zurich, Switzerland, 2014.
  2. L. Vuong, J. Brandt, Z. Lin, L. Bourdev, and T. S. Huang, “Interactive facial feature localization,” in Proceedings of the European Conference on Computer Vision, pp. 679–692, Springer, Florence, Italy, October 2012.
  3. H.-S. Lee and D. Kim, “Tensor-based AAM with continuous variation estimation: application to variation-robust face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1102–1116, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. T. Weise, S. Bouaziz, L. Hao, and M. Pauly, “Realtime performance-based facial animation,” ACM Transactions on Graphics, vol. 30, no. 4, p. 77, 2011. View at Google Scholar
  5. S. W. Chew, P. Lucey, S. Lucey, J. Saragih, J. F. Cohn, and S. Sridharan, “Person-independent facial expression detection using constrained local models,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG '11), pp. 915–920, IEEE, Santa Barbara, Calif, USA, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Huang, X. Ding, and C. Fang, “Pose robust face tracking by combining view-based AAMs and temporal filters,” Computer Vision and Image Understanding, vol. 116, no. 7, pp. 777–792, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '09), pp. 1794–1801, IEEE, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Gao, I. W.-H. Tsang, L.-T. Chia, and P. Zhao, “Local features are not lonely—Laplacian sparse coding for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 3555–3561, IEEE, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. 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
  10. 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, IEEE, Providence, RI, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. D.-S. Pham, O. Arandjelovic, and S. Venkatesh, “Achieving stable subspace clustering by post-processing generic clustering results,” in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN '16), pp. 2390–2396, IEEE, Vancouver, Canada, July 2016. View at Publisher · View at Google Scholar
  12. T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Active appearance models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681–685, 2001. View at Publisher · View at Google Scholar · View at Scopus
  13. M. F. Hansen, J. Fagertun, and R. Larsen, “Elastic appearance models,” in Proceedings of the 22nd British Machine Vision Conference (BMVC '11), September 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. P. A. Tresadern, M. C. Ionita, and T. F. Cootes, “Real-time facial feature tracking on a mobile device,” International Journal of Computer Vision, vol. 96, no. 3, pp. 280–289, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “Robust and efficient parametric face alignment,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 1847–1854, IEEE, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Tzimiropoulos and M. Pantic, “Optimization problems for fast AAM fitting in-the-wild,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 593–600, IEEE, Sydney, Australia, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. M. H. Nguyen and F. De la Torre, “Local minima free parameterized appearance models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, IEEE, Anchorage, Alaska, USA, June 2008.
  18. M. H. Nguyen and F. De La Torre, “Learning image alignment without local minima for face detection and tracking,” in Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG '08), pp. 1–7, IEEE, Amsterdam, Netherlands, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. N. M. Hoai and F. De la Torre, “Metric learning for image alignment,” International Journal of Computer Vision, vol. 88, no. 1, pp. 69–84, 2010. View at Google Scholar
  20. T. F. Cootes, G. V. Wheeler, K. N. Walker, and C. J. Taylor, “View-based active appearance models,” Image and Vision Computing, vol. 20, no. 9-10, pp. 657–664, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Vogler, Z. Li, A. Kanaujia, S. Goldenstein, and D. Metaxas, “The best of both worlds: combining 3D deformable models with active shape models,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07), pp. 1–7, IEEE, Rio de Janeiro, Brazil, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. X. Yu, J. Huang, S. Zhang, W. Yan, and D. N. Metaxas, “Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model,” in Proceedings of the 14th IEEE International Conference on Computer Vision (ICCV '13), pp. 1944–1951, IEEE, Sydney, Australia, December 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models-their training and application,” in Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38–59, 1995. View at Google Scholar
  24. L. Liang, R. Xiao, F. Wen, and J. Sun, “Face alignment via component-based discriminative search,” in Computer Vision—ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part II, vol. 5303 of Lecture Notes in Computer Science, pp. 72–85, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  25. J. M. Saragih, S. Lucey, and J. F. Cohn, “Deformable model fitting by regularized landmark mean-shift,” International Journal of Computer Vision, vol. 91, no. 2, pp. 200–215, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  26. H. Gao, H. K. Ekenel, and R. Stiefelhagen, “Face alignment using a ranking model based on regression trees,” in Proceedings of the British Machine Vision Conference (BMVC '12), pp. 1–11, London, UK, September 2012.
  27. N. Duffy and D. Helmbold, “Boosting methods for regression,” Machine Learning, vol. 47, no. 2-3, pp. 153–200, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  28. X. P. Burgos-Artizzu, P. Perona, and P. Dollár, “Robust face landmark estimation under occlusion,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '13), pp. 1513–1520, Sydney, Australia, 2013.
  29. P. Dollár, P. Welinder, and P. Perona, “Cascaded pose regression,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1078–1085, IEEE, San Francisco, Calif, USA, June 2010.
  30. X. Cao, Y. Wei, F. Wen, and J. Sun, “Face alignment by explicit shape regression,” International Journal of Computer Vision, vol. 107, no. 2, pp. 177–190, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. X. Xiong and F. De La Torre, “Supervised descent method and its applications to face alignment,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 532–539, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in Proceedings of the 27th Asilomar Conference on Signals, Systems & Computers, pp. 40–44, Pacific Grove, Calif, USA, November 1993. View at Scopus
  33. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. X. Gao, Y. Su, X. Li, and D. Tao, “A review of active appearance models,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 40, no. 2, pp. 145–158, 2010. View at Publisher · View at Google Scholar · View at Scopus