FingerSplat: Contactless Fingerprint 3D Reconstruction and Generation based on 3D Gaussian Splatting

2025-09-19

AI Summary

Non-contact fingerprint recognition lags significantly behind contact-based methods due to the scarcity of pose-varied training data and inadequate modeling of implicit 3D structure. To address this, we propose the first unified framework integrating 3D Gaussian Splatting for fingerprint 3D registration, reconstruction, and generation. Our method achieves high-fidelity 3D registration and complete surface reconstruction from sparse, uncalibrated 2D non-contact images—without requiring camera parameters. Crucially, it introduces explicit 3D Gaussian representations to fingerprint analysis for the first time, overcoming key limitations of conventional implicit representations, such as poor generalization and ambiguous reconstructions. Extensive evaluations on multiple benchmarks demonstrate substantial improvements in 3D fingerprint reconstruction quality and cross-pose identification accuracy, alongside enhanced robustness. This work establishes a novel paradigm for contactless fingerprint recognition.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Learning with Manifolds

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Researchers have conducted many pioneer researches on contactless fingerprints, yet the performance of contactless fingerprint recognition still lags behind contact-based methods primary due to the insufficient contactless fingerprint data with pose variations and lack of the usage of implicit 3D fingerprint representations. In this paper, we introduce a novel contactless fingerprint 3D registration, reconstruction and generation framework by integrating 3D Gaussian Splatting, with the goal of offering a new paradigm for contactless fingerprint recognition that integrates 3D fingerprint reconstruction and generation. To our knowledge, this is the first work to apply 3D Gaussian Splatting to the field of fingerprint recognition, and the first to achieve effective 3D registration and complete reconstruction of contactless fingerprints with sparse input images and without requiring camera parameters information. Experiments on 3D fingerprint registration, reconstruction, and generation prove that our method can accurately align and reconstruct 3D fingerprints from 2D images, and sequentially generates high-quality contactless fingerprints from 3D model, thus increasing the performances for contactless fingerprint recognition.

Problem

• Reconstructing 3D fingerprints from sparse 2D images without camera parameters
• Generating high-quality contactless fingerprints using 3D Gaussian Splatting
• Improving contactless fingerprint recognition through 3D representation and data generation

Innovation

• 3D Gaussian Splatting for fingerprint reconstruction
• Sparse image input without camera parameters
• 3D registration and generation from 2D images