Abstract
This paper presents a new method to track both the face pose and the face animation with a monocular camera. The approach is based on the 3D face model CANDIDE and on the SIFT (Scale Invariant Feature Transform) descriptors, extracted around a few given landmarks (26 selected vertices of CANDIDE model) with a Bayesian approach. The training phase is performed on a synthetic database generated from the first video frame. At each current frame, the face pose and animation parameters are estimated via a Bayesian approach, with a Gaussian prior and a Gaussian likelihood function whose the mean and the covariance matrix eigenvalues are updated from the previous frame using eigen decomposition. Numerical results on pose estimation and landmark locations are reported using the Boston University Face Tracking (BUFT) database and Talking Face video. They show that our approach, compared to six other published algorithms, provides a very good compromise and presents a promising perspective due to the good results in terms of landmark localization.
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References
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: TPAMI, pp. 484–498 (1998)
Xiao, J., Baker, S., Matthews, I., Kanade, T.: Real-time combined 2d+3d active appearance models. In: CVPR (2004)
Gross, R., Matthews, I., Baker, S.: Active appearance models with occlusion. IVC 24, 593–604 (2006)
Saragih, J.M., Lucey, S., Cohn, J.F.: Deformable model fitting by regularized landmark mean-shift. IJCV 91, 200–215 (2011)
Cascia, M.L., Sclaroff, S., Athitsos, V.: Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models. IEEE Trans. PAMI 22, 322–336 (2000)
Xiao, J., Moriyama, T., Kanade, T., Cohn, J.: Robust full-motion recovery of head by dynamic templates and re-registration techniques. International Journal of Imaging Systems and Technology 13, 85–94 (2003)
Morency, L.P., Whitehill, J., Movellan, J.R.: Generalized adaptive view-based appearance model: Integrated framework for monocular head pose estimation. In: FG (2008)
Vacchetti, L., Lepetit, V., Fua, P.: Stable real-time 3d tracking using online and offline information. IEEE Trans. PAMI 26, 1385–1391 (2004)
DeCarlo, D., Metaxas, D.N.: Optical flow constraints on deformable models with applications to face tracking. IJCV 38, 99–127 (2000)
Chen, Y., Davoine, F.: Simultaneous tracking of rigid head motion and non-rigid facial animation by analyzing local features statistically. In: BMVC (2006)
Ybáñez-Zepeda, J.A., Davoine, F., Charbit, M.: Local or global 3d face and facial feature tracker. In: ICIP, vol. 1, pp. 505–508 (2007)
Lefevre, S., Odobez, J.M.: Structure and appearance features for robust 3d facial actions tracking. In: ICME (2009)
FaceAPI, http://www.seeingmachines.com
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Ahlberg, J.: Candide-3 - an updated parameterised face. Technical report, Dept. of Electrical Engineering, Linkping University, Sweden (2001)
Dementhon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. IJCV 15, 123–141 (1995)
Golub, G., Kahan, W.: Calculating the singular values and pseudo-inverse of a matrix. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis 2, 205–224 (1965)
Nelder, J.A., Mead, R.: A simplex algorithm for function minimization. Computer Journal, 308–313 (1965)
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Tran, NT., Ababsa, FE., Charbit, M., Feldmar, J., Petrovska-Delacrétaz, D., Chollet, G. (2013). 3D Face Pose and Animation Tracking via Eigen-Decomposition Based Bayesian Approach. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_55
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DOI: https://doi.org/10.1007/978-3-642-41914-0_55
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