Fingerprint Matching Incorporating Ridge Features With Minutiae

Abstract

This paper introduces a novel fingerprint matching algorithm using both ridge features and the conventional minutiae feature to increase the recognition performance against nonlinear deformation in fingerprints. The proposed ridge features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type. These ridge features have some advantages in that they can represent the topology information in entire ridge patterns existing between two minutiae and are not changed by nonlinear deformation of the finger. For extracting ridge features, we also define the ridge-based coordinate system in a skeletonized image. With the proposed ridge features and conventional minutiae features (minutiae type, orientation, and position), we propose a novel matching scheme using a breadth-first search to detect the matched minutiae pairs incrementally. Following that, the maximum score is computed and used as the final matching score of two fingerprints. Experiments were conducted for the FVC2002 and FVC2004 databases to compare the proposed method with the conventional minutiae-based method. The proposed method achieved higher matching scores. Thus, we conclude that the proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used in conjunction with existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.