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A System Theoretic Approach to Synthesis and Classification of Lip Articulation

A System Theoretic Approach to Synthesis and Classification of Lip Articulation. H .E. Ç eting ü l , R.A. Chaudhry, R. Vidal Center for Imaging Science, Johns Hopkins University. International Workshop on Dynamical Vision at ICCV 2007. Presentation Outline. Aim and Motivation.

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A System Theoretic Approach to Synthesis and Classification of Lip Articulation

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  1. A System Theoretic Approach to Synthesis and Classification of Lip Articulation H.E.Çetingül, R.A. Chaudhry, R. Vidal Center for Imaging Science, Johns Hopkins University International Workshop on Dynamical Vision at ICCV 2007

  2. Presentation Outline

  3. Aim and Motivation

  4. Previous Work & Contributions 1T.Chen, “Audiovisual Speech Processing,” IEEE SPM 2001. 2H.E.Cetingul, E.Erzin, Y.Yemez, A.M.Tekalp, “Discriminative analysis of lip motion features for speaker identification and speech-reading,” IEEE TIP 15(10), 2006.

  5. System Overview Lip Sequences Feature Extraction Lip Trajectories System Parameters of Lip Dynamics Lip Database Speaker/Speech Recognition Lip Movement Synthesis

  6. Representation of Lip Articulation (1/2) 1N.Eveno, A.Caplier, P.-Y.Coulon, “Accurate and quasi-automatic lip tracking,” IEEE TCSVT 14(5), 2004.

  7. Representation of Lip Articulation (2/2)

  8. Dynamical Systems for Lip Articulation

  9. ARMA System Identification (SID) 1P.V.Overschee, B.D.Moor, “N4SID:…,” Automatica, 1994. 2G.Doretto, A.Chiuso, Y.Wu, S.Soatto, “Dynamic textures,” IJCV 51(2), 2003.

  10. Synthesis with ARMA parameters (1/2)

  11. Synthesis with ARMA parameters (2/2)

  12. Classification of Lip Articulation

  13. Distances and Nearest Neighbor (NN) 1K.D.Cock, B.D.Moor, “Subspace angles and distances between ARMA models,” System and Control Letters 46(4), 2002.

  14. Kernels and Support Vector Machines (1/2) 1R.J.Martin, “A metric for ARMA processes,” IEEE TSP 48(4), 2000. 2A.B.Chan, N.Vasconcelos, “Probabilistic kernels for the classification of autoregressive visual processes,” IEEE CVPR 2005.

  15. Kernels and Support Vector Machines (2/2) 1S.Vishwanathan, A.Smola, R.Vidal, “Binet-Cauchy kernels on dynamical systems and its applications to the analysis of dynamic scenes,” IJCV 73(1), 2006.

  16. Experimental Details

  17. Classification Results

  18. Classification Results

  19. Synthesis Results video01 video02 video03

  20. Conclusions & Future Work

  21. Thanks

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