Abstract
This paper presents a novel method for face alignment under unknown head poses and nonrigid warp, within the framework of markov random field. The proposed method learns a 3D face shape model comprised of 31 facial features and a texture model for each facial feature from a 3D face database. The models are combined to serve as the unary, pairwise and high order constraints of the markov random field. The facial features are located by minimizing the potential function of markov random field, which is solved with dual decomposition. The main contribution of this paper is composed of three aspects. First, Random Project Tree is utilized to learn the manifold structure of the facial feature appearance under different view points. Second, a 3D face shape model is learned to capture the linear part of face shape distribution due to the change of identity and expression, which is served as the high order constraints. Third, Markov random field is introduced to model the nonlinear part of the face shape distribution, and also to deal with occlusion of facial features due to head pose variation or ornaments. Experiments was taken on the Texas 3D face database and face images downloaded from the Internet, results shows capability of adapting large head pose variations of the method.
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
Preview
Unable to display preview. Download preview PDF.
References
Guidi, G., Vedaldi, A., Soatto, S.: Joint data alignment up to (lossy) transformations. In: CVPR (2008)
Berg, A.C., Malik, J.: Geometric blur for template matching. In: CVPR (2001)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: SIGGRAPH (1999)
Cootes, T.F., Taylor, C.J.: An algorithm for tuning an active appearance model to new data. In: BMVC (2006)
Li, S.Z., Zhang, J., Wang, J.: Nearest manifold approach for face recognition. In: FG (2004)
Lucey, S., Cox, M., Sridharan, S., Cohn, J.: Least squares congealing for unsupervised alignment of images. In: CVPR (2008)
Matthews, I., Baker, S.: Active appearancemodels revisited. In: IJCV (2004)
Fisher, J., Cippola, R., Aranjelovic, O., Shakhnarovich, G., Darrell, T.: Face recognition with image sets using manifold density divergence. In: CVPR (2005)
Markey, M.K., Gupta, S., Bovik, A.C.: Anthropometric 3d face recognition. International Journal of Computer Vision (2010)
Markey, M.K., Gupta, S., Bovik, A.C.: Texas 3d face recognition database. Image Analysis & Interpretation, SSIAI (2010)
Kittler, J., Arashloo, S.R., Christmas, W.J.: Facial feature localization using graph matching with higher order statistical shape priors and global optimization. In: BTAS (2010)
Gong, S., Romdhani, S., Psarrou, A.: A multi-view nonlinear active shape model using kernel pca. In: British Machine Vision Conference (1999)
Sattar, A., Seguier, R.: Mvaam (multi-view active appearance model) optimized by multi-objective genetic algorithm. Automatic Face & Gesture Recognition (2008)
Cooper, D., Cootes, T., Taylor, C., Graham, J.: Active shape models - their training and application. In: Computer Vision and Image Understanding (1995)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, p. 484. Springer, Heidelberg (1998)
Cooper, D.H., Cootes, T.F., Taylor, C.J., Graham, J.: Training models of shape from sets of examples. In: BMVC (1992)
Hu, Y., Niyogi, P., He, X., Yan, S., Zhang, H.: Face recognition using laplacianfaces. In: PAMI (2005)
Kabra, M., Freund, Y., Dasgupta, S., Verma, N.: Learning the structure of manifolds using random projections. In: Neural Information Processing Systems, NIPS (2007)
Zhang, L., Ai, H.: Multi-view active shape model with robust parameter estimation. In: ICCV (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiong, R., Wang, J., Chu, J. (2013). Face Alignment Based on 3D Face Shape Model and Markov Random Field. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-33932-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33931-8
Online ISBN: 978-3-642-33932-5
eBook Packages: EngineeringEngineering (R0)