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)