Exploring ridge curvature for fingerprint indexing

One of the main challenges in building an efficient and scalable automatic fingerprint identification system is to identify features which are highly discriminative and are reproducible across different prints of the same finger. Most existing fingerprint matching approaches rely on minutiae geometry. Relatively, little effort has gone into analyzing ridge flow patterns present in the fingerprint, partly due to difficulty in extracting robust discriminative features from the fingerprint images. In this paper, we analyze the usefulness of ridge curvature information for fingerprint matching and classification applications. Specifically, for an indexing framework, we explore whether the curvature information can be utilized along with the existing minutiae geometry-based features for further reducing the number of potential candidates for fingerprint identification. Experimental results indicate the robustness of the proposed curvature-based characterization and its usefulness in improving the efficiency of existing fingerprint-based identification systems.