On guided model-based analysis for ear biometrics

Abstract

Ears are a new biometric with a major advantage in that they appear to maintain their structure with increasing age. Current approaches have exploited 2D and 3D images of the ear in human identification. Contending that the ear is mainly a planar shape we use 2D images, which are consistent with deployment in surveillance and other planar-image scenarios. So far ear biometric approaches have mostly capitalized on general properties and overall appearance of ear images, and the details of the ear structure have been little discussed. Using the embryological studies of the ear development, which reveal a component-wise structure for the ear, we propose a new model-based approach. Our model is a part-wise description of the ear derived by a stochastic clustering on a set of scale invariant features of a training set. We further extend our model description, by a wavelet-based analysis with a specific aim of capturing information in the ear’s boundary structures, which can augment discriminant variability. In recognition, ears are automatically enroled and then recognized via the parts selected by the model. The incorporation of the wavelet-based analysis of the outer ear structures forms an extended or hybrid method. By results, both in modelling and recognition, our new model-based approach does indeed appear to be a promising new approach to ear biometrics. Recognizing the occlusion by hair as one of the main obstacles hindering the deployment of ear biometrics, we have specifically chosen our techniques to provide performance advantages in occlusion. We shall present a thorough evaluation of performance in occlusion, using a robust PCA for comparison purposes. Our new hybrid method does indeed appear to be a promising new approach to ear biometrics, by guiding a model-based analysis via anatomical knowledge.