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.