In human facial behavioral analysis, Action Unit (AU) coding is a powerful instrument to cope with the diversity of facial expressions. Almost all of the work in the literature for facial action recognition is based on 2D camera images. Given the performance limitations in AU detection with 2D data, 3D facial surface information appears as a viable alternative. 3D systems capture true facial surface data and are less disturbed by illumination and head pose. In this paper we extensively compare the use of 3D modality vis-a-vis 2D imaging modality for AU recognition. Surface ` data is converted into curvature data and mapped into 2D so that both modalities can be compared on a fair ground. Since the approach is totally data-driven, possible bias due to the design is avoided. Our experiments cover 25 AUs and is based on the comparison of Receiver Operating Characteristic (ROC) curves. We demonstrate that in general 3D data performs better, especially for lower face AUs. Furthermore it is more robust in detecting low intensity AUs. Also, we show that generative and discriminative classifiers perform on a par with 3D data. Finally, we evaluate fusion of the two modalities. The highest detection rate was achieved by fusion, which is 97.1 area under the ROC curve. This score was 95.4 for 3D and 93.5 for 2D modality.