A B-Spline Function Based 3D Point Cloud Unwrapping Scheme for 3D Fingerprint Recognition and Identification

Abstract

A three-dimensional (3D) fingerprint recognition and identification system offers several advantages: in addition to sharing the hygiene property of a 2D contactless fingerprint system in reducing the risk of contamination, it offers an exceptional anti-proofing attack capability over the traditional two-dimensional (2D) fingerprint, including 2D contactless fingerprint, recognition and identification systems. This is because capturing a 3D fingerprint sample will require a synchronized operation of multiple 3D-spaced cameras. It is infeasible to construct a quality 3D fingerprint sample based on a set of random 2D fingerprint images. In addition to capturing surface ridge and valley patterns similar to a 2D fingerprint system, 3D fingerprints capture depth, curvature, and shape information, enabling the development of more precise and robust authentication systems. Despite recent advancements, significant challenges remain. The topological height of fingerprint pixels complicates the extraction of ridge and valley patterns. Furthermore, registration issues limit the acquisition process, requiring consistent direction and orientation across all samples. To address these challenges, this article introduces a method that unwraps 3D fingerprints, represented as 3D point clouds, using B-spline curve fitting to mitigate height variation and reduce registration limitations. The unwrapped point cloud is then converted into a grayscale image by mapping the relative heights of the points. This grayscale image is subsequently used for recognition through conventional 2D fingerprint identification methods. The proposed approach demonstrated superior performance in 3D fingerprint recognition, achieving Equal Error Rates (EERs) of 0.2072%, 0.26%, and 0.22% across three experiments, outperforming existing methods. Additionally, the method surpassed 3D fingerprint flattening technique in both recognition and identification during cross-session experiments, achieving an EER of 1.50% when fingerprints with varying registrations were included.