Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion

2026-05-15

AI Summary

This work addresses the poor compatibility between contactless 3D fingerprint systems and conventional 2D fingerprint recognition, as well as the challenge of cross-modal matching. To overcome these limitations, the authors propose a non-parametric unified framework that does not rely on a global finger model. By fusing multi-view 3D point clouds and employing pose-aware unwrapping, elliptical fitting for pose normalization, and a cross-modal registration strategy, the method achieves high-fidelity conversion and precise alignment from 3D fingerprints to their 2D equivalents. Evaluated on a self-collected multimodal database of 150 fingers, the system demonstrates a 3D fusion error of approximately 0.09 mm and ridge-level 3D–2D registration accuracy, significantly enhancing matching performance in real-world scenarios.

Abstract

Three-dimensional (3D) fingerprints preserve global finger geometry and local ridge structure while avoiding contact-induced deformation, but they remain difficult to integrate with legacy two-dimensional (2D) fingerprint systems. This paper addresses the intermediate stage between 3D acquisition and cross-modal matching, and presents a unified framework for 3D fingerprint preprocessing and registration across contactless and contact-based 2D modalities. The framework combines four components: 1) a nonparametric visualization and unwrapping method that converts a 3D fingerprint point cloud into a rolled-equivalent 2D representation without relying on a global finger-shape model; 2) a point-cloud fusion pipeline that registers and mosaics multiple partial 3D captures into a more complete fingerprint model; 3) an ellipse-based pose normalization method for canonical finger alignment; and 4) a pose-aware cross-modal registration strategy that improves compatibility between 3D fingerprints and both contactless and contact-based 2D fingerprints. Experiments on a self-collected multimodal fingerprint database containing 150 fingers show that the proposed framework achieves ridge-level 3D registration accuracy, robust pose estimation, and consistent gains in 2D compatibility. In particular, the 3D fusion error is concentrated around 0.09 mm, contactless 2D--3D registration reaches ridge-scale projection accuracy, and pose-aware unwrapping improves genuine matching scores relative to generic 3D unwrapping. These results support the use of 3D fingerprints as an effective geometric bridge across heterogeneous fingerprint modalities.

Problem

• cross-modal registration
• 3D fingerprint
• 2D fingerprint
• pose normalization
• fingerprint compatibility

Innovation

• cross-modal registration
• 3D fingerprint unwrapping
• point-cloud fusion
• pose-aware normalization
• fingerprint modality bridging