Most automatic facial expression analysis systems try to analyze emotion categories. However, psychologists argue that there is no straight forward way to classify emotions from facial expressions. Instead, they propose FACS (Facial Action Coding System), a de-facto standard for categorizing facial actions independent from emotional categories. We describe a system that recognizes asymmetric FACS Action Unit activities and intensities without the use of markers. Facial expression extraction is achieved by difference images that are projected into a sub-space using either PCA or ICA, followed by nearest neighbor classification. Experiments show that this holistic approach achieves a recognition performance comparable to marker-based facial expression analysis systems or human FACS experts for a single-subject database recorded under controlled conditions.