Print2Volume: Generating Synthetic OCT-based 3D Fingerprint Volume from 2D Fingerprint Image

• URL: http://arxiv.org/abs/2508.21371v1
• Date: Fri, 29 Aug 2025 07:26:39 GMT
• Title: Print2Volume: Generating Synthetic OCT-based 3D Fingerprint Volume from 2D Fingerprint Image
• Authors: Qingran Miao, Haixia Wang, Haohao Sun, Yilong Zhang,
• Abstract summary: This paper introduces Print2Volume, a novel framework for generating realistic, synthetic 3D fingerprints from 2D fingerprint image.<n>We generated a large-scale synthetic dataset of 420,000 samples.<n>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)
• Score: 5.697372349288325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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