用3D高斯泼溅实现无接触指纹的3D重建与生成,提升识别性能。
FingerSplat: Contactless Fingerprint 3D Reconstruction and Generation based on 3D Gaussian Splatting
- 首次将3D高斯泼溅用于指纹识别,实现稀疏图像下的3D重建。
- 无需相机参数信息,仅用2张图即可准确对齐和重建3D指纹。
- 适合需要高精度无接触指纹识别的安防与生物认证场景。
尽管已有大量关于无接触指纹的研究,但其识别性能仍落后于接触式方法,主要因缺乏带姿态变化的无接触指纹数据,以及未充分利用隐式3D指纹表示。本文提出一种结合3D高斯泼溅的新框架,实现无接触指纹的3D注册、重建与生成,为无接触指纹识别提供新范式。据我们所知,这是首个将3D高斯泼溅应用于指纹识别的工作,也是首个在稀疏输入图像下、无需相机参数信息的情况下实现有效3D注册与完整重建的方法。在3D指纹注册、重建与生成上的实验表明,该方法能从2D图像中准确对齐并重建3D指纹,并基于3D模型生成高质量无接触指纹,从而显著提升无接触指纹识别性能。
原文摘要 · Abstract (English)
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.
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