Geometry-assisted statistical modeling for face mosaicing

The modeling of facial appearance has many applications. This paper proposes an approach to generating a statistical face model based on video mosaicing. Unlike traditional video mosaicing, we use the geometry of a face to improve the mosaicing result. Given a face sequence, each frame is unwrapped onto certain portion of the surface of a sphere, as determined by spherical projection and the minimization procedure using the Levenberg-Marquardt algorithm. A statistical model containing a mean image and a number of eigenimages, instead of only one image template, is used to represent the face mosaic. Good experimental results have been observed.

[1]  Tsuhan Chen,et al.  Tracking of multiple faces for human-computer interfaces and virtual environments , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[2]  David Banks,et al.  Interactive shape metamorphosis , 1995, I3D '95.

[3]  David G. Stork,et al.  Pattern Classification , 1973 .

[4]  B. V. K. Vijaya Kumar,et al.  Face authentication for multiple subjects using eigenflow , 2003, Pattern Recognit..

[5]  Demetri Terzopoulos,et al.  Realistic modeling for facial animation , 1995, SIGGRAPH.

[6]  Richard Szeliski,et al.  Video mosaics for virtual environments , 1996, IEEE Computer Graphics and Applications.

[7]  Rama Chellappa,et al.  3D face reconstruction from video using a generic model , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[8]  Tsuhan Chen,et al.  Second-generation error concealment for video transport over error prone channels , 2002, Proceedings. International Conference on Image Processing.

[9]  Arun Ross,et al.  Fingerprint mosaicking , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Timothy F. Cootes,et al.  Learning to identify and track faces in image sequences , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[12]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[13]  Michael J. Black,et al.  Robust principal component analysis for computer vision , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  D. Skočaj.  Weighted Incremental Subspace Learning , 2002 .

[15]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.