A common approach in fingerprint matching algorithms consists of minimizing a similarity measure between feature vectors of both images, over a set of linear transformations of one image to the other. In this work we propose the thin-plate spline as a more accurate model for the geometric transformations that arise in fingerprint images. In addition we show how such a model can be integrated into a matching algorithm by means of a two-step iterative minimization with auxiliary variables. Such a method allows to correct many of the false pairings of minutiae commonly found by matching algorithms based on linear transforms.
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