Computer Vision

Living Edition

Face Alignment

  • Amit KumarEmail author
  • Rama Chellappa
Living reference work entry
DOI: https://doi.org/10.1007/978-3-030-03243-2_879-1
  • 16 Downloads

Related Concepts

Definition

Face alignment refers to transforming a given face image to a canonical coordinate system. This is done by automatically detecting facial fiducial points also called facial landmarks or keypoints and then using standard transformation methods such as affine/similarity transformation. These fiducial points are predefined points on the face image which are mainly located or centered around facial parts such as the eyes, nose, chin, and mouth corners as shown in Fig. 1.
This is a preview of subscription content, log in to check access.

References

  1. 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 ChallengeGoogle Scholar
  2. 2.
    Burgos-Artizzu XP, Perona P, Dollar P (2013) Robust face landmark estimation under occlusion. In: International conference on computer vision, pp 1513–1520Google Scholar
  3. 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 2879–2886Google Scholar
  4. 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 ’18Google Scholar
  5. 5.
    Cootes T, Edwards G, Taylor C (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell PAMI 23(6):681–685CrossRefGoogle Scholar
  6. 6.
    Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1):38–59CrossRefGoogle Scholar
  7. 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 1851–1858Google Scholar
  8. 8.
    Saragih JM, Lucey S, Cohn JF (2011) Deformable model fitting by regularized landmark mean-shift. Int J Comput Vis 91(2):200–215MathSciNetCrossRefGoogle Scholar
  9. 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 3444–3451CrossRefGoogle Scholar
  10. 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 354–361Google Scholar
  11. 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 545–552Google Scholar
  12. 12.
    Cao X, Wei Y, Wen F, Sun J (2014) Face alignment by explicit shape regression. Int J Comput Vis 107(2):177–190MathSciNetCrossRefGoogle Scholar
  13. 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 recognitionCrossRefGoogle Scholar
  14. 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 1685–1692Google Scholar
  15. 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):2037–2041CrossRefGoogle Scholar
  16. 16.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 60(2):91–110CrossRefGoogle Scholar
  17. 17.
    Kumar A, Ranjan R, Patel VM, Chellappa R (2016) Face alignment by local deep descriptor regression. CoRR, abs/1601.07950Google Scholar
  18. 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):272–291MathSciNetCrossRefGoogle Scholar
  19. 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 258–265Google Scholar
  20. 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 79:49–62CrossRefGoogle Scholar
  21. 21.
    Zhu S, Li C, Change Loy C, Tang X (2015) Face alignment by coarse-to-fine shape searchingGoogle Scholar
  22. 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 visionCrossRefGoogle Scholar
  23. 23.
    Jourabloo A, Liu X (2015) Pose-invariant 3D face alignment. In: International conference on computer vision, Santiago, ChileCrossRefGoogle Scholar
  24. 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 VegasCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of MarylandCollege ParkUSA

Section editors and affiliations

  • Rama Chellappa
    • 1
  1. 1.University of MarylandCollege ParkUSA