AAM has been successfully applied to face alignment,
but its performance is very sensitive to initial values. In case the initial
values are a little far distant from the global optimum values, there
exists a pretty good possibility that AAM-based face alignment may
converge to a local minimum. In this paper, we propose a progressive
AAM-based face alignment algorithm which first finds the feature
parameter vector fitting the inner facial feature points of the face and
later localize the feature points of the whole face using the first
information. The proposed progressive AAM-based face alignment
algorithm utilizes the fact that the feature points of the inner part of the
face are less variant and less affected by the background surrounding
the face than those of the outer part (like the chin contour). The
proposed algorithm consists of two stages: modeling and relation
derivation stage and fitting stage. Modeling and relation derivation
stage first needs to construct two AAM models: the inner face AAM
model and the whole face AAM model and then derive relation matrix
between the inner face AAM parameter vector and the whole face
AAM model parameter vector. In the fitting stage, the proposed
algorithm aligns face progressively through two phases. In the first
phase, the proposed algorithm will find the feature parameter vector
fitting the inner facial AAM model into a new input face image, and
then in the second phase it localizes the whole facial feature points of
the new input face image based on the whole face AAM model using
the initial parameter vector estimated from using the inner feature
parameter vector obtained in the first phase and the relation matrix
obtained in the first stage. Through experiments, it is verified that the
proposed progressive AAM-based face alignment algorithm is more
robust with respect to pose, illumination, and face background than the
conventional basic AAM-based face alignment algorithm.
[1] S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004.
[2] S. Z. Li, Y. S. Cheng, H. J. Zhang, Q. S. Cheng "Multi-view face
alignment using direct appearance models," Automatic Face and Gesture
Recognition, 2002. Proceedings. Fifth IEEE Int-l Conf. on Automatic
Face and Gesture Recognition, pp. 309 - 314, May 2002.
[3] F. Jiao, S. Z. Li, H-Y. Shum, D. Schuurmans, "Face Alignment Using
Statistical Models and Wavelet Features," Proc. 2003 IEEE Computer
Society Conf. on Computer Vision and Pattern Recognition, Vol.1, pp.
I-321 - I-327, June, 2003.
[4] Y. Huang, S. Lin, S.Z. Li, H. Lu, H.-Y. Shum, "Face Alignment Under
Variable Illumination," Proc. Sixth IEEE Int-l Conf. on Automatic Face
and Gesture Recognition, pp. 85-90, May 2004.
[5] S. Xin and H. Ai, "Face Alignment under Various Poses and
Expressions," ACII2005, LNCS 3784, pp. 40-47, 2005.
[6] L. Zhang et al., "Robust Face Alignment Based on Local Texture
Classifiers," ICIP 2005. IEEE Int-l Conf. on Image Processing, Vol. 2,
pp.354-357, Sep. 2005.
[7] G. Edwards, C. J. Taylor, and T. F. Cootes, "Interpreting Face Images
using Active Appearance Models," in Proc. IEEE Int. Conf. Automatic
Face and Gesture Recognition, pp. 300-30, 1998.
[8] T. F. Cootes, G. J. Edwards, C. J. Taylor, H. Burkhardt, and B. Neuman,
"Active Appearance Models," in Proc. Eur. Conf. Computer Vision, vol.
2, pp. 484-498, 1998.
[9] T. F. Cootes, D. J. Edwards, and S. J. Taylor, "Active Appearance
Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp.
681-685, Jun. 2001..
[10] M. B. Stegmann, B. K. Ersboll, R. Larsen, "FAME -- A Flexible
Appearance Modelling Environment," IEEE Transactions on Medical
Imaging, Vol. 22, Iss.10, pp. 1319 - 1331, Oct. 2003.
[11] I. Matthews and S. Baker, "Active Appearance Models Revisited,"
International Journal of Computer Vision, Vol. 60, No. 2, pp. 135 - 164,
Nov. 2004.
[12] A.U Batur and M.H. Hayes, "Adaptive Active Appearance Models,"
IEEE Transactions on Image Processing, Vol. 14, Issue 11, pp. 1707 -
1721, Nov. 2005.
[13] D. Cristinacce, T. Cootes, and I. Scott, "A Multi-Stage Approach to Facial
Feature Detection," Proc. British Machine Vision Conference 2004,
Vol.1, pp.277-286.
[14] Stephen Boyd and Lieven Vandenberghe, Convex Optimization,
Cambridge University Press
[15] J. C. Gower, "Generalized Procrustes Analysis," Psychometrika,
40:33--51, 1975.
[16] D. T. Lee and B. J. Schachter, "Two Algorithms for Constructing a
Delaunay Triangulation," Int. J. Computer Information Sci. 9,
pp.219-242, 1980.
[17] IMM face database and AAM-API, http://www2.imm.dtu.dk/~aam/
[1] S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004.
[2] S. Z. Li, Y. S. Cheng, H. J. Zhang, Q. S. Cheng "Multi-view face
alignment using direct appearance models," Automatic Face and Gesture
Recognition, 2002. Proceedings. Fifth IEEE Int-l Conf. on Automatic
Face and Gesture Recognition, pp. 309 - 314, May 2002.
[3] F. Jiao, S. Z. Li, H-Y. Shum, D. Schuurmans, "Face Alignment Using
Statistical Models and Wavelet Features," Proc. 2003 IEEE Computer
Society Conf. on Computer Vision and Pattern Recognition, Vol.1, pp.
I-321 - I-327, June, 2003.
[4] Y. Huang, S. Lin, S.Z. Li, H. Lu, H.-Y. Shum, "Face Alignment Under
Variable Illumination," Proc. Sixth IEEE Int-l Conf. on Automatic Face
and Gesture Recognition, pp. 85-90, May 2004.
[5] S. Xin and H. Ai, "Face Alignment under Various Poses and
Expressions," ACII2005, LNCS 3784, pp. 40-47, 2005.
[6] L. Zhang et al., "Robust Face Alignment Based on Local Texture
Classifiers," ICIP 2005. IEEE Int-l Conf. on Image Processing, Vol. 2,
pp.354-357, Sep. 2005.
[7] G. Edwards, C. J. Taylor, and T. F. Cootes, "Interpreting Face Images
using Active Appearance Models," in Proc. IEEE Int. Conf. Automatic
Face and Gesture Recognition, pp. 300-30, 1998.
[8] T. F. Cootes, G. J. Edwards, C. J. Taylor, H. Burkhardt, and B. Neuman,
"Active Appearance Models," in Proc. Eur. Conf. Computer Vision, vol.
2, pp. 484-498, 1998.
[9] T. F. Cootes, D. J. Edwards, and S. J. Taylor, "Active Appearance
Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp.
681-685, Jun. 2001..
[10] M. B. Stegmann, B. K. Ersboll, R. Larsen, "FAME -- A Flexible
Appearance Modelling Environment," IEEE Transactions on Medical
Imaging, Vol. 22, Iss.10, pp. 1319 - 1331, Oct. 2003.
[11] I. Matthews and S. Baker, "Active Appearance Models Revisited,"
International Journal of Computer Vision, Vol. 60, No. 2, pp. 135 - 164,
Nov. 2004.
[12] A.U Batur and M.H. Hayes, "Adaptive Active Appearance Models,"
IEEE Transactions on Image Processing, Vol. 14, Issue 11, pp. 1707 -
1721, Nov. 2005.
[13] D. Cristinacce, T. Cootes, and I. Scott, "A Multi-Stage Approach to Facial
Feature Detection," Proc. British Machine Vision Conference 2004,
Vol.1, pp.277-286.
[14] Stephen Boyd and Lieven Vandenberghe, Convex Optimization,
Cambridge University Press
[15] J. C. Gower, "Generalized Procrustes Analysis," Psychometrika,
40:33--51, 1975.
[16] D. T. Lee and B. J. Schachter, "Two Algorithms for Constructing a
Delaunay Triangulation," Int. J. Computer Information Sci. 9,
pp.219-242, 1980.
[17] IMM face database and AAM-API, http://www2.imm.dtu.dk/~aam/
@article{"International Journal of Information, Control and Computer Sciences:63983", author = "Daehwan Kim and Jaemin Kim and Seongwon Cho and Yongsuk Jang and Sun-Tae Chung and Boo-Gyoun Kim", title = "Progressive AAM Based Robust Face Alignment", abstract = "AAM has been successfully applied to face alignment,
but its performance is very sensitive to initial values. In case the initial
values are a little far distant from the global optimum values, there
exists a pretty good possibility that AAM-based face alignment may
converge to a local minimum. In this paper, we propose a progressive
AAM-based face alignment algorithm which first finds the feature
parameter vector fitting the inner facial feature points of the face and
later localize the feature points of the whole face using the first
information. The proposed progressive AAM-based face alignment
algorithm utilizes the fact that the feature points of the inner part of the
face are less variant and less affected by the background surrounding
the face than those of the outer part (like the chin contour). The
proposed algorithm consists of two stages: modeling and relation
derivation stage and fitting stage. Modeling and relation derivation
stage first needs to construct two AAM models: the inner face AAM
model and the whole face AAM model and then derive relation matrix
between the inner face AAM parameter vector and the whole face
AAM model parameter vector. In the fitting stage, the proposed
algorithm aligns face progressively through two phases. In the first
phase, the proposed algorithm will find the feature parameter vector
fitting the inner facial AAM model into a new input face image, and
then in the second phase it localizes the whole facial feature points of
the new input face image based on the whole face AAM model using
the initial parameter vector estimated from using the inner feature
parameter vector obtained in the first phase and the relation matrix
obtained in the first stage. Through experiments, it is verified that the
proposed progressive AAM-based face alignment algorithm is more
robust with respect to pose, illumination, and face background than the
conventional basic AAM-based face alignment algorithm.", keywords = "Face Alignment, AAM, facial feature detection,
model matching.", volume = "1", number = "9", pages = "2881-5", }