Abstract
It is a challenging vision problem to discover non-rigid shape deformation for an image ensemble belonging to a single object class, in an automatic or semi-supervised fashion. The conventional semi- supervised approach [1] uses a congealing-like process to propagate manual landmark labels from a few images to a large ensemble. Although effective on an inter-person database with a large population, there is potential for increased labeling accuracy. With the goal of providing highly accurate labels, in this paper we present a parametric curve representation for each of the seven major facial contours. The appearance information along the curve, named curve descriptor, is extracted and used for congealing. Furthermore, we demonstrate that advanced features such as Histogram of Oriented Gradient (HOG) can be utilized in the proposed congealing framework, which operates in a dual-curve congealing manner for the case of a closed contour. With extensive experiments on a 300-image ensemble that exhibits moderate variation in facial pose and shape, we show that substantial progress has been achieved in the labeling accuracy compared to the previous state-of-the-art approach.
Chapter PDF
Similar content being viewed by others
References
Tong, Y., Liu, X., Wheeler, F.W., Tu, P.: Automatic facial landmark labeling with minimal supervision. In: CVPR (2009)
Miller, E., Matsakis, N., Viola, P.: Learning from one example through shared densities on transforms. In: CVPR, vol. 1, pp. 464–471 (2000)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE T-PAMI 23, 681–685 (2001)
Matthews, I., Baker, S.: Active appearance models revisited. IJCV 60, 135–164 (2004)
Liu, X.: Discriminative face alignment. IEEE T-PAMI 31, 1941–1954 (2009)
Dalal, N., Triggs, W.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)
Vetter, T., Jones, M.J., Poggio, T.: A bootstrapping algorithm for learning linear models of object classes. In: CVPR, pp. 40–46 (1997)
Learned-Miller, E.: Data driven image models through continuous joint alignment. IEEE T-PAMI 28, 236–250 (2006)
Cox, M., Sridharan, S., Lucey, S., Cohn, J.: Least squares congealing for unsupervised alignment of images. In: CVPR (2008)
Balci, S., Golland, P., Shenton, M., Wells, W.: Free-form B-spline deformation model for groupwise registration. In: MICCAI, pp. 23–30 (2007)
Baker, S., Matthews, I., Schneider, J.: Automatic construction of active appearance models as an image coding problem. IEEE T-PAMI 26, 1380–1384 (2004)
Kokkinos, I., Yuille, A.: Unsupervised learning of object deformation models. In: ICCV (2007)
Cootes, T., Twining, C., Petrovic, V., Schestowitz, R., Taylor, C.: Groupwise construction of appearance models using piece-wise affine deformations. In: BMVC, vol. 2, pp. 879–888 (2005)
Cristinacce, D., Cootes, T.: Facial motion analysis using clustered shortest path tree registration. In: Proc. of the 1st Int. Workshop on Machine Learning for Vision-based Motion Analysis with ECCV (2008)
Torre, F., Nguyen, M.: Parameterized kernel principal component analysis: Theory and applications to supervised and unsupervised image alignment. In: CVPR (2008)
Langs, G., Donner, R., Peloschek, P., Horst, B.: Robust autonomous model learning from 2D and 3D data sets. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 968–976. Springer, Heidelberg (2007)
Saragih, J., Goecke, R.: A nonlinear discriminative approach to AAM fitting. In: ICCV (2007)
Sidorov, K., Richmond, S., Marshall, D.: An efficient stochastic approach to groupwise non-rigid image registration. In: CVPR (2009)
Meltzer, J., Soatto, S.: Edge descriptors for robust wide-baseline correspondence. In: CVPR (2008)
Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. IJCV 56, 221–255 (2004)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models — their training and application. CVIU 61, 38–59 (1995)
Liu, X., Yu, T., Sebastian, T., Tu, P.: Boosted deformable model for human body alignment. In: CVPR (2008)
Liu, X., Tong, Y., Wheeler, F.W.: Simultaneous alignment and clustering for an image ensemble. In: ICCV (2009)
Kasinski, A., Florek, A., Schmidt, A.: The PUT face database. Technical report, Poznan University of Technology, Poznan, Poland (2009)
Schneiderman, H.: Learning a restricted Bayesian network for object detection. In: CVPR (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, X., Tong, Y., Wheeler, F.W., Tu, P.H. (2010). Facial Contour Labeling via Congealing. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15549-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-15549-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15548-2
Online ISBN: 978-3-642-15549-9
eBook Packages: Computer ScienceComputer Science (R0)