We describe a novel approach for 3D ear biometrics using video. A series of frames are extracted from a video clip and the region-of-interest (ROI) in each frame is independently reconstructed in 3D using Shape from Shading (SFS). The resulting 3D models are then registered using the Iterative Closest Point (ICP) algorithm. We iteratively consider each model in the series as a reference and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips. The results (95.0% rank-1 recognition rate) indicate that the proposed approach can produce recognition rates comparable to systems that use 3D range data. To the best of our knowledge, we are the first to develop a 3D ear biometric system that obtains 3D ear structure from a video sequence.