A Novel Matching Algorithm for Distorted Fingerprints Based on Penalized Quadratic Model

At present, one of the most challenging problems in fingerprint recognition is the matching of distorted fingerprints. In this paper, the authors propose penalized quadratic model to deal with the non-linear distortion. Firstly, minutiae as well as sampling points on all the ridges are employed to represent fingerprint. Secondly, similarity between minutiae is estimated by their neighboring sampling points. Thirdly, greedy matching algorithm is adopted to establish the initial minutiae correspondences which are used to select landmarks to calculate the quadratic model parameters. At last, input fingerprint is warped and matching process is conducted again to obtain similarity score between warped fingerprint and template fingerprint.