Physical Analogies for Ear Recognition

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Description/Abstract

Hurley et al [1,2,3] have developed a pair of invertible linear transforms called the force field transform and potential energy transform which transforms an ear image into a force field by pretending that pixels have a mutual attraction propor-tional to their intensities and inversely to the square of the distance between them rather like Newton's Law of Universal Gravitation. Underlying this force field there is an associated potential energy field which in the case of an ear takes the form of a smooth surface 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. Since the transform also turns out to be invertible, all of the original information is preserved and since the otherwise smooth surface is modulated by these peaks and ridges, it is argued that much of the information is transferred to these features and that therefore they should make good features. An analysis of the mechanism of this algorithmic field line feature extraction approach leads to a more powerful method called convergence feature extraction based on the diver-gence of force direction revealing even more information in the form of anti-wells and anti-channels.