Improving 3D Finger Traits Recognition via Generalizable Neural Rendering
Hongbin Xu, Junduan Huang, Yuer Ma, Zifeng Li, Wenxiong Kang
TL;DR
This work tackles the bottleneck of explicit 3D reconstruction in finger biometrics by introducing FingerNeRF, an implicit neural rendering framework that learns a generalizable 3D representation from sparse views. Central to the approach are the Trait Guided Transformer and two geometric priors—Depth Distillation Loss and Trait Guided Rendering Loss—that address shape-radiance ambiguity by leveraging finger trait correspondences. Across three datasets spanning finger images and finger vein modalities, FingerNeRF consistently outperforms explicit reconstruction baselines and other generalizable NeRFs in both recognition accuracy and rendering quality. The results demonstrate the practical impact of implicit 3D representations for open-set, few-view finger biometrics, with strong gains in verification and identification metrics and robust cross-modality transfer.
Abstract
3D biometric techniques on finger traits have become a new trend and have demonstrated a powerful ability for recognition and anti-counterfeiting. Existing methods follow an explicit 3D pipeline that reconstructs the models first and then extracts features from 3D models. However, these explicit 3D methods suffer from the following problems: 1) Inevitable information dropping during 3D reconstruction; 2) Tight coupling between specific hardware and algorithm for 3D reconstruction. It leads us to a question: Is it indispensable to reconstruct 3D information explicitly in recognition tasks? Hence, we consider this problem in an implicit manner, leaving the nerve-wracking 3D reconstruction problem for learnable neural networks with the help of neural radiance fields (NeRFs). We propose FingerNeRF, a novel generalizable NeRF for 3D finger biometrics. To handle the shape-radiance ambiguity problem that may result in incorrect 3D geometry, we aim to involve extra geometric priors based on the correspondence of binary finger traits like fingerprints or finger veins. First, we propose a novel Trait Guided Transformer (TGT) module to enhance the feature correspondence with the guidance of finger traits. Second, we involve extra geometric constraints on the volume rendering loss with the proposed Depth Distillation Loss and Trait Guided Rendering Loss. To evaluate the performance of the proposed method on different modalities, we collect two new datasets: SCUT-Finger-3D with finger images and SCUT-FingerVein-3D with finger vein images. Moreover, we also utilize the UNSW-3D dataset with fingerprint images for evaluation. In experiments, our FingerNeRF can achieve 4.37% EER on SCUT-Finger-3D dataset, 8.12% EER on SCUT-FingerVein-3D dataset, and 2.90% EER on UNSW-3D dataset, showing the superiority of the proposed implicit method in 3D finger biometrics.