On Model-Based Analysis of Ear Biometrics
Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Most current approaches are holistic and describe the ear by its general properties. The authors propose a new model-based approach, capitalizing on explicit structure and with the advantages of being robust in noise and occlusion. The model is a constellation of generalized ear parts, which is learned off-line using an unsupervised learning algorithm over an enrolled training set of 63 ear images. The Scale Invariant Feature Transform (SIFT), is used to detect the features within the ear images. In recognition, given a profile image of the human head, the ear is enrolled and recognized from the parts selected via the model.