3D Face Pose Tracking from Monocular Camera via Sparse Representation of Synthesized Faces

Ngoc-Trung Tran, Jacques Feldmar, Maurice Charbit, Dijana Petrovska-Delacrétaz, Gérard Chollet

Abstract

This paper presents a new method to track head pose efficiently from monocular camera via sparse representation of synthesized faces. In our framework, the appearance model is trained using a database of synthesized face generated from the first video frame. The pose estimation is based on the similarity distance between the observations of landmarks and their reconstructions. The reconstruction is the texture extracted around the landmark, represented as a sparse linear combination of positive training samples after solving l1-norm problem. The approach finds the position of new landmarks and face pose by minimizing an energy function as the sum of these distances while simultaneously constraining the shape by a 3D face. Our framework gives encouraging pose estimation results on the Boston University Face Tracking (BUFT) dataset.

References

  1. Ahlberg, J. (2001). Candide-3 - an updated parameterised face. Technical report, Dept. of Electrical Engineering, Linkping University, Sweden.
  2. Cascia, M. L., Sclaroff, S., and Athitsos, V. (2000). Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models. IEEE Trans. PAMI, 22(4):322-336.
  3. Chen, Y. and Davoine, F. (2006). Simultaneous tracking of rigid head motion and non-rigid facial animation by analyzing local features statistically. In BMVC.
  4. Cootes, T. F., Edwards, G. J., and Taylor, C. J. (1998). Active appearance models. TPAMI, pages 484-498.
  5. Cootes, T. F. and Taylor, C. J. (1992). Cj.taylor, ”active shape models - ”smart snakes. In BMVC.
  6. DeCarlo, D. and Metaxas, D. N. (2000). Optical flow constraints on deformable models with applications to face tracking. IJCV, 38(2):99-127.
  7. Dementhon, D. F. and Davis, L. S. (1995). Model-based object pose in 25 lines of code. IJCV, 15:123-141.
  8. Elad, M. and Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12):3736-3745.
  9. Gross, R., Matthews, I., and Baker, S. (2006). Active appearance models with occlusion. IVC, 24(6):593-604.
  10. Lefevre, S. and Odobez, J.-M. (2009). Structure and appearance features for robust 3d facial actions tracking. In ICME.
  11. Mei, X. and Ling, H. (2011). Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. PAMI, 33(11):2259-2272.
  12. Morency, L.-P., Whitehill, J., and Movellan, J. R. (2008). Generalized adaptive view-based appearance model: Integrated framework for monocular head pose estimation. In FG.
  13. Nelder, J. A. and Mead, R. (1965). A simplex algorithm for function minimization. Computer Journal, pages 308-313.
  14. Saragih, J. M., Lucey, S., and Cohn, J. F. (2011). Deformable model fitting by regularized landmark meanshift. IJCV, 91:200-215.
  15. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society (Series B), 58:267-288.
  16. Vacchetti, L., Lepetit, V., and Fua, P. (2004). Stable realtime 3d tracking using online and offline information. IEEE Trans. PAMI, 26(10):1385-1391.
  17. Wright, J., Yang, A., Ganesh, A., Sastry, S., and Ma, Y. (2009). Robust face recognition via sparse representation. TPAMI, 31(2):210 -227.
  18. Xiao, J., Baker, S., Matthews, I., and Kanade, T. (2004). Real-time combined 2d+3d active appearance models. In CVPR.
  19. Xiao, J., Moriyama, T., Kanade, T., and Cohn, J. (2003). Robust full-motion recovery of head by dynamic templates and re-registration techniques. International Journal of Imaging Systems and Technology, 13:85 - 94.
  20. Yang, A. Y., Ganesh, A., Zhou, Z., Sastry, S., and Ma, Y. (2010). A review of fast l1-minimization algorithms for robust face recognition. CoRR, abs/1007.3753.
  21. Ybán˜ez-Zepeda, J. A., Davoine, F., and Charbit, M. (2007). Local or global 3d face and facial feature tracker. In ICIP, volume 1, pages 505-508.
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Paper Citation


in Harvard Style

Tran N., Feldmar J., Charbit M., Petrovska-Delacrétaz D. and Chollet G. (2013). 3D Face Pose Tracking from Monocular Camera via Sparse Representation of Synthesized Faces . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 328-333. DOI: 10.5220/0004345003280333


in Bibtex Style

@conference{visapp13,
author={Ngoc-Trung Tran and Jacques Feldmar and Maurice Charbit and Dijana Petrovska-Delacrétaz and Gérard Chollet},
title={3D Face Pose Tracking from Monocular Camera via Sparse Representation of Synthesized Faces},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={328-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004345003280333},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - 3D Face Pose Tracking from Monocular Camera via Sparse Representation of Synthesized Faces
SN - 978-989-8565-48-8
AU - Tran N.
AU - Feldmar J.
AU - Charbit M.
AU - Petrovska-Delacrétaz D.
AU - Chollet G.
PY - 2013
SP - 328
EP - 333
DO - 10.5220/0004345003280333