Skip to main content

Face Alignment

  • Living reference work entry
  • First Online:
Computer Vision
  • 157 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. Sagonas C, Antonakos E, Tzimiropoulos G, Zafeiriou S, Pantic M (2016) 300 faces in-the-wild challenge: database and results. Image Vis Comput 47:3–18. 300-W, the First Automatic Facial Landmark Detection in-the-Wild Challenge

    Google Scholar 

  2. Burgos-Artizzu XP, Perona P, Dollar P (2013) Robust face landmark estimation under occlusion. In: International conference on computer vision, pp 1513–1520

    Google Scholar 

  3. Zhu X, Ramanan D (2012) Face detection, pose estimation, and landmark localization in the wild. In: IEEE conference on computer vision and pattern recognition, pp 2879–2886

    Google Scholar 

  4. Kumar A, Chellappa R (2018) Disentangling 3D pose in a dendritic CNN for unconstrained 2D face alignment. In: IEEE conference on computer vision and pattern recognition, CVPR ’18

    Google Scholar 

  5. Cootes T, Edwards G, Taylor C (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell PAMI 23(6):681–685

    Article  Google Scholar 

  6. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  7. Tzimiropoulos G, Pantic M (2014) Gauss-Newton deformable part models for face alignment in-the-wild. In: IEEE conference on computer vision and pattern recognition, pp 1851–1858

    Google Scholar 

  8. Saragih JM, Lucey S, Cohn JF (2011) Deformable model fitting by regularized landmark mean-shift. Int J Comput Vis 91(2):200–215

    Article  MathSciNet  Google Scholar 

  9. Asthana A, Zafeiriou S, Cheng S, Pantic M (2013) Robust discriminative response map fitting with constrained local models. In: IEEE conference on computer vision and pattern recognition, CVPR ’13, Washington, DC. IEEE Computer Society, pp 3444–3451

    Chapter  Google Scholar 

  10. Baltrusaitis T, Robinson P, Morency L (2013) Constrained local neural fields for robust facial landmark detection in the wild. In: 2013 IEEE International conference on computer vision workshops, pp 354–361

    Google Scholar 

  11. Belhumeur PN, Jacobs DW, Kriegman DJ, Kumar N (2011) Localizing parts of faces using a consensus of exemplars. In: IEEE conference on computer vision and pattern recognition, CVPR ’11, Washington, DC. IEEE Computer Society, pp 545–552

    Google Scholar 

  12. Cao X, Wei Y, Wen F, Sun J (2014) Face alignment by explicit shape regression. Int J Comput Vis 107(2):177–190

    Article  MathSciNet  Google Scholar 

  13. Xiong X, De la Torre F (2013) Supervised descent method and its application to face alignment. In: IEEE conference on computer vision and pattern recognition

    Book  Google Scholar 

  14. Ren S, Cao X, Wei Y, Sun J (2014) Face alignment at 3000 FPS via regressing local binary features. In: IEEE conference on computer vision and pattern recognition, pp 1685–1692

    Google Scholar 

  15. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  16. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 60(2):91–110

    Article  Google Scholar 

  17. Kumar A, Ranjan R, Patel VM, Chellappa R (2016) Face alignment by local deep descriptor regression. CoRR, abs/1601.07950

    Google Scholar 

  18. Chen J-C, Ranjan R, Sankaranarayanan S, Kumar A, Chen C-H, Patel VM, Castillo CD, Chellappa R (2018) Unconstrained still/video-based face verification with deep convolutional neural networks. Int J Comput Vis 126(2):272–291

    Article  MathSciNet  Google Scholar 

  19. Kumar A, Alavi A, Chellappa R (2017) Kepler: keypoint and pose estimation of unconstrained faces by learning efficient H-CNN regressors. In: 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017), pp 258–265

    Google Scholar 

  20. Kumar A, Alavi A, Chellappa R (2018) Kepler: simultaneous estimation of keypoints and 3D pose of unconstrained faces in a unified framework by learning efficient H-CNN regressors. Image Vis Comput 79:49–62

    Article  Google Scholar 

  21. Zhu S, Li C, Change Loy C, Tang X (2015) Face alignment by coarse-to-fine shape searching

    Google Scholar 

  22. Bulat A, Tzimiropoulos G (2017) Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: International conference on computer vision

    Book  Google Scholar 

  23. Jourabloo A, Liu X (2015) Pose-invariant 3D face alignment. In: International conference on computer vision, Santiago, Chile

    Book  Google Scholar 

  24. Jourabloo A, Liu X (2016) Large-pose face alignment via CNN-based dense 3D model fitting. In: IEEE conference on computer vision and pattern recognition, Las Vegas

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amit Kumar .

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Kumar, A., Chellappa, R. (2020). Face Alignment. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_879-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03243-2_879-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics