Weight Thin-Plate Spline Fingerprint Matching Using Minutiae Locations and Orientations

Dealing with nonlinear deformation is one of the challenges in fingerprint matching. Nonlinear deformation, arising from the plasticity of the skin, and the pressure and the other factors during acquisition, lead to difficulties in establishing a match between multiple impressions from the same fingerprint. One solution to deal with the nonlinear deformation is to estimate a model to describe it, like thin-plate spline model (TPS). The match method based on model could be described as the following steps. First, local match algorithm is performed on input image and template image to obtain some corresponding minutiae. Then, the corresponding points are used to calculate the parameters of the model. Based on the model, two images are aligned and more minutiae was matched. Repeat the second and third steps until no more minutiae are matched. In the original TPS model, the differences between every two corresponding points from the local match are ignored. Meanwhile, minutiae orientation is not taken into account. In this paper a weighted thin-plate spline model using both the difference between corresponding points and the minutiae orientation was proposed to describe the fingerprint deformation, and the performance of the fingerprint match algorithm based on the new model on databases of FVC2004 turned to be efficient and robust.