Advertisement

Robust Multi-view Face Alignment Based on Cascaded 2D/3D Face Shape Regression

  • Fuxuan Chen
  • Feng Liu
  • Qijun ZhaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9967)

Abstract

In this paper, we present a cascaded regression algorithm for multi-view face alignment. Our method employs a two-stage cascaded regression framework and estimates 2D and 3D facial feature points simultaneously. In stage one, 2D and 3D facial feature points are roughly detected on the input face image, and head pose analysis is applied based on the 3D facial feature points to estimate its head pose. The face is then classified into one of three categories, namely left profile faces, frontal faces and right profile faces, according to its pose. In stage two, accurate facial feature points are detected by using an appropriate regression model corresponding to the pose category of the input face. Compared with existing face alignment methods, our proposed method can better deal with arbitrary view facial images whose yaw angles range from −90 to \(90^{\circ }\). Moreover, in order to enhance its robustness to facial bounding box variations, we randomly generate multiple bounding boxes according to the statistical distributions of bounding boxes and use them for initialization during training. Extensive experiments on public databases prove the superiority of our proposed method over state-of-the-art methods, especially in aligning large off-angle faces.

Keywords

Face alignment Multi-view Cascaded regression Pose analysis 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61202161) and the National Key Scientific Instrument and Equipment Development Projects of China (No. 2013YQ49087904).

References

  1. 1.
    Zhou, S., Comaniciu, D.: Shape regression machine. In: Information Proceeding in Medical, Imaging, pp. 13–25 (2007)Google Scholar
  2. 2.
    Jourabloo, A., Liu, X.: Pose-invariant 3D face alignment. In: ICCV, pp. 3694–3702 (2015)Google Scholar
  3. 3.
    Liu, F., Zeng, D., Zhao, Q., Liu, X.: Joint face alignment and 3D face reconstruction. In: ECCV (2016, in press)Google Scholar
  4. 4.
    Dollar, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR, pp. 1078–1085 (2010)Google Scholar
  5. 5.
    Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: CVPR, pp. 532–539 (2013)Google Scholar
  6. 6.
    Tulyakov, S., Sebe, N.: Regressing a 3D face shape from a single image. In: ICCV, pp. 1109–1119 (2015)Google Scholar
  7. 7.
    Yu, X., Huang, J., Zhang, S., Yan, W., Metaxas, D.N.: Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model. In: ICCV, pp. 1994–1951 (2013)Google Scholar
  8. 8.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report 07–49 (2007)Google Scholar
  9. 9.
    Blanz, V., Vetter, T.: A morphable model for the sunthesis of 3D faces. In: SIGGRAPH 1999, pp. 187–194 (1999)Google Scholar
  10. 10.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR, pp. 2879–2886 (2012)Google Scholar
  11. 11.
    Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: 300 Faces in-the-wild challenge: the first facial landmark localization challenge. In: ICCV-W, pp. 397–403 (2013)Google Scholar
  12. 12.
    Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: CVPR, pp. 3476–3483 (2013)Google Scholar
  13. 13.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. In: Proceedings of IEEE International Conference on Automatic Face Gesture Recognition, In Image and Vision Computing, pp. 807–813 (2010)Google Scholar
  14. 14.
    Yu, X., Huang, J., Zhang, S., Yan, W., Metaxas, D.N.: Pose-free facial landmark fitting via optimized part mixtures and cascaded deformable shape model. In: ICCV, pp. 1944–1951 (2013)Google Scholar
  15. 15.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR, pp. 2879–2886 (2012)Google Scholar
  16. 16.
    Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 94–108. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10599-4_7 Google Scholar
  17. 17.
    Yu, S.: Shenzhen University face detector (2014). https://github.com/ShiqiYu/libfacedetection
  18. 18.
    Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: a 3D solution (2015). arXiv:1511.07212

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer ScienceSichuan UniversityChengduChina

Personalised recommendations