Automatic facial action unit (AU) detection in videos is
the key ingredient to all systems that utilize a subject
face for either interaction or analysis purposes. With
the ever growing range of possible applications,
achieving a high accuracy in the simplest possible manner
gains even more importance. In this paper, we present new
features obtained by applying local binary patterns to
images processed by morphological and bilateral filters.
We use as features the variations of these patterns
between the expressive and neutral faces, and show that
we can gain a considerable amount of accuracy increase by
simply applying these fundamental image processing tools
and choosing the right way of representing the patterns.
We also use these features in conjunction with additional
features based on facial point geometrical relations
between frames and achieve detection rates higher than
methods previously proposed, using a small number of
features and basic support vector machine classification.