On guided model-based analysis for ear biometrics

Abstract

As a biometric, ears have major advantage in that they appear to maintain their shape with increasing age. Current approaches have exploited both 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 also consistent with deployment in surveillance and other planar image scenarios. Capitalizing on explicit structures, we propose a new parts-based model which has an advantage in handling noise and occlusion. Our model is learned via a stochastic clustering algorithm and a training set of ear images. In this, the candidates for the model parts are detected using the Scale Invariant Feature Transform (SIFT). We shall review different accounts of ear formation and consider some congenital ear anomalies which discuss apportioning various components to the ear’s complex structure, and illustrate that our parts-based approach is in accordance with this component-wise structure. In recognition, the ears are automatically enrolled and recognized from the parts selected via the model. The performance is evaluated on test sets selected from XM2VTS data. The model achieves promising results recognizing unoccluded ears and for occluded samples its performance is evaluated against PCA and a robust PCA. By results, both in modelling and recognition, our new model-based method does indeed appear to be a promising new approach to ear biometrics

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ORCID for Mark Nixon: orcid.org/0000-0002-9174-5934

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