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

2026-04-17

AI Summary

This study addresses the challenges in 3D fingerprint recognition arising from topological height variations and inconsistent acquisition orientations, which hinder reliable ridge–valley extraction and render alignment highly sensitive. To overcome these issues, the authors propose a novel point cloud unwrapping method based on B-spline curve fitting. This approach, for the first time, applies B-spline functions to 3D fingerprint point clouds to effectively eliminate height discrepancies and mitigate orientation dependency. The resulting unwrapped surface is then mapped into a grayscale image, enabling seamless integration into established 2D fingerprint recognition pipelines. Experimental results demonstrate that the method achieves equal error rates (EER) as low as 0.2072%–0.26% across three standard benchmarks and an EER of 1.50% in cross-session, non-aligned scenarios, significantly outperforming existing 3D flattening techniques.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Learning with Manifolds
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Three-dimensional (3D) fingerprint recognition and identification offer several advantages over traditional two-dimensional (2D) recognition systems. The contactless nature of 3D fingerprints enhances hygiene and security, reducing the risk of contamination and spoofing. In addition to surface ridge and valley patterns, 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 paper 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.

Problem

• 3D fingerprint recognition
• point cloud unwrapping
• height variation
• registration issues
• ridge extraction

Innovation

• B-spline
• 3D point cloud unwrapping
• 3D fingerprint recognition
• height variation mitigation
• registration-invariant