AU Recognition on 3D Faces Based On An Extended Statistical Facial Feature Model
Abstract
Recognition of facial action units (AU) is one of
two main streams in the facial expressions analysis. Action units
deform facial appearance simultaneously in landmark locations
and local texture as well as geometry on 3D faces. Thus,
it is necessary to use features extracted from multiple facial
modalities to characterize these deformations comprehensively.
In order to fuse the contribution of the discriminative power
from all features efficiently, we propose to use our extended
statistical facial feature models (SFAM) to generate feature
instances corresponding to AU class for each feature. Then
the similarity between each feature on a face and its instances
are evaluated so that a set of similarity scores are obtained. All
sets of scores on the face are then weighted for AU recognition.
Experiments on the Bosphorus database show its state-of-the-
art performance.