Abstract
We describe a computer vision system for observing the
“action units” of a face using video sequences as input. The
visual observation (sensing) is achieved by using an optimal estimation
optical flow method coupled with a geometric and a physical (muscle)
model describing the facial structure. This modeling results in a
time-varying spatial patterning of facial shape and a parametric
representation of the independent muscle action groups, responsible for
the observed facial motions. These muscle action patterns may then be
used for analysis, interpretation, and synthesis. Thus, by interpreting
facial motions within a physics-based optimal estimation framework, a
new control model of facial movement is developed. The newly extracted
action units (which we name “FACS+”) are both physics and
geometry-based, and extend the well-known FACS parameters for facial
expressions by adding temporal information and non-local spatial
patterning of facial motion
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