FingerSplat: Contactless Fingerprint 3D Reconstruction and Generation based on 3D Gaussian Splatting
This addresses the performance gap between contactless and contact-based fingerprint recognition for biometric applications, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of insufficient data and lack of 3D representations in contactless fingerprint recognition by introducing a framework for 3D registration, reconstruction, and generation using 3D Gaussian Splatting, which improves recognition performance.
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.