verifiedOpen Access Peer-Reviewed Scientific Journal (CC BY 4.0)
Submit Manuscript|OJS Login|Register
Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
search
person
Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 121-125

Tracking feature points of the talking face

Noureddine CHERABIT •
Amar DJERADI •
Fatma Zohra CHELALI
Publication DateDecember 16, 2016
Volume / IssueVol. 4, No. 4 (pp. 121-125)
subject

Abstract

This paper presents a method of tracking points on a speaking face and the reconstruction of a face model from speaking descriptive vectors followed. After capturing a video of a talking face using a CCD camera, his facial expression changes in a video sequences. The essencial of this work is to track the feature points on the face using the Lucas-Kanade method, then use descriptive vectors up to be exploited for the reconstruction of a model of speaking face. The tracking method used is based on the theorem of Lucas-Kanade and facial reconstruction based on the method of delaunay triangulation.
Keywords:talking headautomatic tracking pointscoding of visual speechdelaunay triangulationLucas-Kanade
format_list_numbered

References (24)

Cited Literature
  1. 1

    Lucas, Bruce david. Generalized image matching by the method of differences. Phd thesis, university microfilms international. 1985.

  2. 2

    R.vaillant et I. surin. Face Reconstruction Through Active Stereovision. Traitement du Signal 1995.

  3. 3

    Ashok Samal and Prasana Iyengar. Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition, 25(1) 65-77, 1992.

  4. 4

    N. Sarris and M. G. Strintzis. 3D Modeling and Animation: Synthesis and Analysis Techniques for the Human Body. Published in the United States of America by IRM Press. 2005.

  5. 5

    E. H. Adelson and J. R. Bergen. Spatiotemporal energy models for the perception of vision. Journal of Optical Society of America, A2: 284{299, February 1985.

  6. 6

    J.Y.A. Wang and E.H. Adelson. Spatio-temporal segmentation of video data. In Proceedings of SPIE on Image and Video Processing II, 2182, pages 120{131, San Jose, February 1994.

  7. 7

    Y. Weiss and E.H. Adelson. Perceptually organized em: A framework for motion segmentation that combines information about form and motion. Technical Report 315, MIT Media Lab Perceptual Computing Section TR, 1994.

  8. 8

    D.J. Heeger. Optical flow using spatiotemporal fillters. International Journal of Computer Vision, vol 1, pp 279- 302, 1988.

  9. 9

    B.K.P Horn and B.G. Schunck. Determining optical flow. Artificial Intelligence, 17 :185-204, 1981.

  10. 10

    J. Weber and J. Malik. Robust computation of optical flow in a multi-scale differential framework. Computer Vision, 14 :67-81, 1995.

  11. 11

    Y.T. Wu, T. Kanade, J. Cohn, and C.C. Li. Optical flow estimation using wavelet motion model. In Sixth International Conference on Computer Vision, pages 992- 998 Narosa Publishing House, 1998.

  12. 12

    J.P. Leduc, F. Mujica, R. Murenzi, and M.J.T. Smith. Spatiotemporal wavelets: A group-theoretic construction for motion estimation and tracking. Siam Journal of Applied Mathematics, 61(2) :596-632, 2000.

  13. 13

    C.P. Bernard. Ondelettes et problèmes mal posés : la mesure du flot optique et l'interpolation irrégulière. PhD thesis, Ecole Polytechnique, CMAP, Centre de Mathématiques Appliquées., Palaiseau, France, Novembre 1999.

  14. 14

    C.M. Lee. Joint source-Channel Coding Tools for Robust Transmission of Video Sequences; Application to H.263+ and H.264. PhD thesis,

  15. 15

    Mathew A. Turk and Alex P. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1): 72- 86, 1991.

  16. 16

    A. Pentland, B. Moghaddam, O. Starner, T. Oliyide and M. Turk. View-Based and Modular Eigenspaces for Face.

  17. 17

    D. Beymer. Face Recognition under Varying Pose. In Computer and Pattern Recognition, June 1994.

  18. 18

    S. Mechkour, R. Boesch et R. Oprea. Interfaces Multimodales. Université de Fribourg – Département d’Informatique. 2009.

  19. 19

    Ma. Pillet. Triangulation de Delaunay. Ecole Normale Supérieure de Cachan, antenne de Bretagne, avril 2010.

  20. 20

    S. W. SLOAN. A fast algorithm for constructing Delaunay triangulations in the plane. Department of C~vil Engineering and Surveying, The University of Newcastle, NSW 2308, Australia, Adv. Eng. Software, 1987, Vol. 9, No. 1.

  21. 21

    R. Cutler and Larry S. Davis. Fellow.Robust Real-Time Periodic Motion Detection, Analysis, and Applications, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 22, NO. 8, AUGUST 2000.

  22. 22

    M. Julien Richefeu, Détection et analyse du mouvement sur système de vision à base de rétine numérique. DOCTEUR de l’UNIVERSITE PARIS 6. Décembre 2006.

  23. 23

    M. gotla and Z. huang. A minimalist approach to facial reconstruction. 1999.

  24. 24

    S. baker AND I. Matthews. Lucas-Kanade 20 Years On: A Unifying Framework. International Journal of Computer Vision 56(3), 221–255, 2004.

format_quote

How to Cite This Article

N. C., A. D., F. Z. C. (2016). Tracking feature points of the talking face. International Journal of Applied Methods in Electronics and Computers, 121-125.