The LIP automatic segmentation algorithm



Automatic Lip Tracking: Bayesian Segmentation and Active Contours In A Cooperative Scheme

Main Authors: M.Liévin and P.Delmas.

Abstract

An algorithm for speaker's lip contour extraction is presented here. A color video sequence
of speaker's face is acquired, under natural lighting conditions and without any particular make-up.
First, a logarithmic color transform is performed from RGB to HI (hue, intensity) color space. A
statistical approach using Markov random field modelling helps to segment the mouth area, integrating red hue and motion into a spatiotemporal neighbourhood. Simultaneously, a Region Of Interest (ROI) and relevant boundaries points are automatically extracted. Next, an active contour using spatially varying coefficients is initialised with the results of the preprocessing stage. Performance of active contours are greatly improved when initialisation is close to the desired
features. Finally, an accurate lip shape with inner and outer borders is obtained with good quality
results in this challenging situation.
 

Results


Top Left: An RVB image sequence of mouth movements.
Top Right: Sequence of final lip shape superposed on the initial sequence.
Bottom Left: Initial points and mouth corners (see P.Delmas)
Bottom Right: Final shape of the mouth using active contours (see P.Delmas)
 


Top Left: An RVB image sequence of mouth movements.
Top Right: Sequence of final lip shape superposed on the initial sequence.
Bottom Left: Initial points and mouth corners (see P.Delmas)
Bottom Right: Final shape of the mouth using active contours (see P.Delmas)