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The creation of facial range models by 3D imaging systems has led to extensive work on 3D face recognition . However, little work has been done to study the usefulness of such data for recognizing and understanding facial expressions. Psychological research shows that the shape of a human face, a highly mobile facial surface, is critical to facial expression perception. In this paper, we investigate the importance and usefulness of 3D facial geometric shapes to represent and recognize facial expressions using 3D facial expression range data. We propose a novel approach to extract primitive 3D facial expression features, and then apply the feature distribution to classify the prototypic facial expressions. In order to validate our proposed approach, we have conducted experiments for person-independent facial expression recognition using our newly created 3D facial expression database. We also demonstrate the advantages of our 3D geometric based approach over 2D texture based approaches in terms of various head poses.