Force Field Feature Extraction for Ear Biometrics

Abstract

The overall objective in defining feature space is to reduce the dimensionality of the original pattern space, whilst maintaining discriminatory power for classification. To meet this objective in the context of ear biometrics a novel force field transformation is introduced in which the image is treated as an array of mutually attracting particles that act as the source of a Gaussian force field. In a similar way to Newton’s Law of Universal Gravitation pixels are imagined to attract each other according to the product of their intensities and inversely to the square of the distance between them. Underlying the force field there is a scalar potential energy field, which in the case of an ear takes the form of a smooth surface that resembles a small mountain with a number of peaks joined by ridges. The peaks correspond to potential energy wells and to extend the analogy the ridges correspond to potential energy channels. The directional properties of the force field are exploited to automatically locate these wells and channels, which then form the basis of a set of characteristic ear features. The new features are robust especially in the presence of noise, and have the advantage that the ear does not need to be explicitly extracted from its background. The directional properties of the ensuing force field lead to two equivalent extraction techniques; one is algorithmic and based on field lines, while the other is analytical and based on the divergence of force direction. The technique is validated by performing recognition on a database of ears selected from the XM2VTS face database. This confirms not only that ears do indeed appear to have potential as a biometric, but also that the new approach is well suited to their description.

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