Directionally Weighted Distance for Minutiae-Triplets Preservation on Elastic Deformation of Fingerprint Recognition

摘要

? 2022Most fingerprint matching methods suffer from elastic deformation of fingerprints, resulting in an increment of the false-rejection rate. We propose a new distance model for the minutiae-triplets formation that can remedy the elastic deformation of fingerprints. The new distance model, called a directionally weighted distance model, provides higher priority to neighboring minutiae within the same direction or the same ridge flow of the observed minutia. We introduce two methods that apply the proposed distance model to the minutiae-triplets formation. While the first method directly applies the model, the second method combines the model with ridge flow to handle highly curved areas such as singular-point areas or highly distorted fingerprint areas. We evaluate the proposed methods using two minutiae-triplets matching algorithms on sixteen public domain fingerprint databases. The experimental results show that the proposed distance model can significantly improve the accuracy of both matching algorithms on most databases.