Abstract
Optical Coherence Tomography (OCT) enables the acquisition of high-resolution, three-dimensional fingerprint data, capturing rich subsurface structures for robust biometric recognition. However, the high cost and time-consuming nature of OCT data acquisition have led to a scarcity of large-scale public datasets, significantly hindering the development of advanced algorithms, particularly data-hungry deep learning models. To address this critical bottleneck, this paper introduces Print2Volume, a novel framework for generating realistic, synthetic OCT-based 3D fingerprints from 2D fingerprint image. Our framework operates in three sequential stages: (1) a 2D style transfer module that converts a binary fingerprint into a grayscale images mimicking the style of a Z-direction mean-projected OCT scan; (2) a 3D Structure Expansion Network that extrapolates the 2D im-age into a plausible 3D anatomical volume; and (3) an OCT Realism Refiner, based on a 3D GAN, that renders the structural volume with authentic textures, speckle noise, and other imaging characteristics. Using Print2Volume, we generated a large-scale synthetic dataset of 420,000 samples. Quantitative experiments demonstrate the high quality of our synthetic data and its significant impact on recognition performance. By pre-training a recognition model on our synthetic data and fine-tuning it on a small real-world dataset, we achieved a remarkable reduction in the Equal Error Rate (EER) from 15.62% to 2.50% on the ZJUT-EIFD benchmark, proving the effectiveness of our approach in overcoming data scarcity.
Abstract (translated)
光学相干断层扫描（OCT）能够获取高分辨率的三维指纹数据，捕捉丰富的亚表面结构，从而实现稳健的生物识别。然而，由于OCT数据采集成本高昂且耗时较长，导致大型公共数据集稀缺，严重阻碍了先进算法尤其是对大量数据有需求的深度学习模型的发展。 为了解决这一关键瓶颈问题，本文提出了Print2Volume框架，该框架可以从二维指纹图像生成真实的基于OCT的三维合成指纹。我们的框架包括三个连续阶段：（1）一个二维风格转换模块，将二值化的指纹图转化为模拟Z方向均值投影的OCT扫描风格的灰度图像；（2）一个3D结构扩展网络，该网络从2D图像推断出合理的真实生物体体积；以及（3）基于三维生成对抗网络（GAN）的OCT现实性增强器，用于以真实的纹理、斑点噪声和其他成像特征来渲染解剖结构。 利用Print2Volume框架，我们创建了一个包含420,000个样本的大规模合成数据集。定量实验显示了我们的合成数据的质量及其对识别性能的重要影响。通过在我们合成的数据上预训练一个识别模型，并使用小规模的真实世界数据进行微调，我们在ZJUT-EIFD基准测试中将等错误率（EER）从15.62%显著降低到了2.50%，证明了我们的方法在克服数据稀缺性方面的有效性。
URL
https://arxiv.org/abs/2508.21371
