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Although a number of different ear recognition techniques have been proposed, not much work has been done in the field of ear detection. In this work we present a new ear detection approach for 3D profile images based on surface curvature and semantic analysis of edge-patterns. The algorithm applies edge-based detection techniques, which are known from 2D approaches, to a 3D data model. As an additional result of the ear detection, the outline of the outer helix is found, which may serve as a basis for further feature extraction steps. As our method does not use a reference ear model, the detector does not need any previous training. Furthermore, the approach is robust against rotation and scale. Experiments using the 3D images from UND-J2 collection resulted in a detection rate of 95.65%.