Abstract
We have implemented methods to reconstruct and model 3D face shapes and to synthesize facial expressions from a set of real human 3D face surface maps. The method employed tensor-based statistical shape modelling and statistical discriminant modelling methods. In the statistical shape modelling approach, new face shapes are created by moving the surface points along the appropriate expressive direction in the training set space. In the statistical discriminant model, new face shapes, such as facial expressions, can be synthesized by moving the surface points along the most discriminant direction found from the classes of expressions in the training set. The advantage of the tensor-based statistical discriminant analysis method is that face shapes of varying degrees can be generated from a small number of examples available in the 3D face shape datasets. The results of the reconstructions and synthesis of three-dimensional faces are illustrated in the paper.
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
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Cootes, T., Walker, K.N., Taylor, C.J.: View-based active appearance models. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 227–232 (2000)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Face recognition using active appearance models. In: ACM European Conference on Computer Vision, vol. 2, pp. 581–695 (1998)
Cootes, T., Lanitis, A.: Statistical models of appearance for computer vision. Technical Report, Draft (2004)
Cootes, T., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Balakrishnama, S., Ganapathriraju, A.: Linear discriminant analysis - a brief tutorial. Institute for Signal and Information Processing, Department of Electrical and Computer Engineering, Mississippi State University, U.S (1998)
Zhao, A., Chellappa, R., Phillips, P.: Subspace linear discriminant analysis for face recognition. Technical Report CAR-TR-914 (1999)
Minoi, J.L., Amin, S.H., Thomaz, C.E., Gillies, D.F.: Synthesizing realistic expressions in 3D face data sets. In: IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems, BTAS (2008)
Thomaz, C.E., Amaral, V., Giraldi, G.A., Kitani, E.C., Sato, J.R., Gillies, D.F.: A multi-linear statistical method for discriminant analysis of 2D frontal face images. In: Mago, V., Bhatia, N. (eds.) book chapter, Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies. IGI Publishing, USA (to appear)
Kitani, E.C., Thomaz, C.E., Gillies, D.F.: A Statistical discriminant model for face interpretation and reconstruction. In: 19th Brazillian Symposium on Computer Graphics and Image Processing (2006)
Vasilescu, M.A.O., Terzopoulos, D.: Multilinear subspace analysis of image ensembles. In: IEEE Conference on Computer Vision and Pattern Recognition (2003)
Lathauwer, L.D., Moor, B.D., Vandewalle, J.: A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications 21(4), 1253–1278 (2000)
Thomaz, C.E., Kitani, E.C., Gillies, D.F.: A maximum uncertainty lda-based approach for limited size problems with applications to face recognition. Journal of the Brazilian Computer Society 12(2), 7–18 (2006)
Wang, J., Yin, L., Wei, X., Sun, Y.: Facial expression recognition based on primitive surface feature distribution. In: The IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2006), New York (2006)
Papatheodorou, T., Rueckert, D.: Evaluation Of 3D Face Recognition Using Registration and PCA. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 997–1009. Springer, Heidelberg (2005)
Minoi, J.L., Gillies, D.F.: A Tensor-based multivariate statistical model for 3D face and facial expression recognition, In: 7th International Conference on Information Technology in Asia (CITA), IEEE Explore (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Minoi, JL., Eduardo Thomaz, C., Gillies, D.F. (2011). Synthesizing 3D Face Shapes Using Tensor-Based Multivariate Statistical Discriminant Methods. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25483-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-25483-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25482-6
Online ISBN: 978-3-642-25483-3
eBook Packages: Computer ScienceComputer Science (R0)