A fingerprint orientation field has distinct characteristics which can differentiate fingerprints from any other flow patterns: it has a specific number of singular points (cores and deltas), the configuration of singular points follows a certain spatial distribution, and its global shape is like an arch. In this paper, we propose a global fingerprint orientation field model, represented in terms of ordinary differential equations, which does not require any prior information such as singular points or orientation of a fingerprint. Further, the model requires only a small number of polynomial terms to represent the global fingerprint orientation field. The coefficients of the model are found subject to the constraints on the total number of singular points (i.e., 0, 2, or 4) in a fingerprint. The proposed model is used to distinguish fingerprint images from non-fingerprint images and altered fingerprints by measuring the abnormality in the orientation field of the image.
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