Abstract
We tackle the facial landmark localization problem as an inference problem over a Markov Random Field. Efficient inference is implemented using Gibbs sampling with approximated full conditional distributions in a latent variable model. This approximation allows us to improve the runtime performance 1000-fold over classical formulations with no perceptible loss in accuracy. The exceptional robustness of our method is realized by utilizing a \(L_{1}\)-loss function and via our new robust shape model based on pairwise topological constraints. Compared with competing methods, our algorithm does not require any prior knowledge or initial guess about the location, scale or pose of the face.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. TCSVT 14, 4–20 (2004)
Mulligan, J.B.: A software-based eye tracking system for the study of air-traffic displays. In: ETRA, ACM, pp. 69–76 (2002)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. TPAMI 23, 681–685 (2001)
Martins, P., Caseiro, R., Henriques, J.F., Batista, J.: Discriminative bayesian active shape models. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 57–70. Springer, Heidelberg (2012)
Zhou, F., Brandt, J., Lin, Z.: Exemplar-based graph matching for robust facial landmark localization. In: ICCV, IEEE, pp. 1025–1032 (2013)
Dollár, P., Welinder, P., Perona, P.: Cascaded pose regression. In: CVPR, IEEE, pp. 1078–1085 (2010)
Dantone, M., Gall, J., Fanelli, G., Van Gool, L.: Real-time facial feature detection using conditional regression forests. In: CVPR, pp. 2578–2585. IEEE (2012)
Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107, 177–190 (2014)
Ren, S., Cao, X., Wei, Y., Sun, J.: Face alignment at 3000 fps via regressing local binary features. In: CVPR, pp. 1685–1692. IEEE (2014)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61, 38–59 (1995)
Arashloo, S.R., Kittler, J., Christmas, W.J.: Facial feature localization using graph matching with higher order statistical shape priors and global optimization. In: BTAS, pp. 1–8. IEEE (2010)
Burgos-Artizzu, X.P., Perona, P., Dollár, P.: Robust face landmark estimation under occlusion. In: ICCV, pp. 1513–1520. IEEE (2013)
Walsh, B.: Markov chain monte carlo and gibbs sampling (2004)
Bishop, C.M., et al.: Pattern Recognition and Machine Learning, vol. 4. Springer, New York (2006)
Komodakis, N., Paragios, N., Tziritas, G.: MRF energy minimization and beyond via dual decomposition. TPAMI 33, 531–552 (2011)
Müller, O., Yang, M.Y., Rosenhahn, B.: Slice sampling particle belief propagation. In: ICCV, pp. 1129–1136. IEEE (2013)
Walker, A.J.: An efficient method for generating discrete random variables with general distributions. TOMS 3, 253–256 (1977)
Yuille, A.L., Hallinan, P.W., Cohen, D.S.: Feature extraction from faces using deformable templates. Int. J. Comput. Vis. 8, 99–111 (1992)
Berg, A.C., Malik, J.: Geometric blur for template matching. In: CVPR, pp. 607–614. IEEE (2001)
Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A.: Face recognition using hog-ebgm. Pattern Recogn. Lett. 29, 1537–1543 (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893. IEEE (2005)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Bromiley, P.: Products and convolutions of gaussian distributions. Medical School, Univ. Manchester, Manchester, UK, Technical report 3 (2003)
Genz, A.: Numerical computation of rectangular bivariate and trivariate normal and t probabilities. Stat. Comput. 14, 251–260 (2004)
Martins, P., Caseiro, R., Batista, J.: Non-parametric bayesian constrained local models. In: CVPR, pp. 1797–1804. IEEE (2014)
Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM face database - an annotated dataset of 240 face images. Technical report, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby (2004)
Brooks, S.P., Gelman, A.: General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vogt, K., Müller, O., Ostermann, J. (2015). Facial Landmark Localization Using Robust Relationship Priors and Approximative Gibbs Sampling. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-27863-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27862-9
Online ISBN: 978-3-319-27863-6
eBook Packages: Computer ScienceComputer Science (R0)