Ear is new class of biometrics that has certain advantages over face and fingerprint which are the two most common biometrics in both academic research and industrial applications. An ear can be imaged in 3D and surface shape information related to its anatomical structure can be obtained. This makes it possible to develop a robust 3D ear biometrics. The talk will present complete human recognition systems based on 3D ear biometrics. It will explore various aspects of 3D ear recognition: representation, detection, recognition, indexing and performance prediction. The experimental results on various large datasets will be presented to demonstrate the effectiveness of the algorithms.