面向光学相干层析指纹的条件扩散生成方法

Conditional Diffusion Generation for Optical Coherence Tomography Fingerprints

摘要

光学相干断层扫描技术以其高分辨率和捕捉指尖皮肤三维结构的能力而闻名,能够增强指纹识别系统的防伪能力.然而,与其它生物识别技术相比,数据集的稀缺严重阻碍其广泛应用.由于采集困难且出于隐私考虑不便公开,研究数据生成是应对该挑战的解决方案之一.本文提出一种基于扩散模型的条件生成方法,利用层分割掩码作为先验知识引导生成过程,通过逐步去噪直接在像素空间建模,避免潜在扩散模型的精度损失,从而生成高保真OCT指纹图像.实验表明,该方法生成的样本具有逼真的皮肤结构特征,通过对30位领域专家进行的主观评价实验证明其生成结果解剖结构清晰、像素分布真实.进一步实验证明,使用生成数据扩充训练集可显著提升多种防伪模型的性能.

Abstract

Optical Coherence Tomography(OCT)is renowned for its high resolution and ability to capture the three-dimensional structure of fingertip skin,significantly enhancing the anti-spoofing capability of fingerprint recognition systems.However,compared to other biometric technologies,the scarcity of datasets severely hinders its widespread application.Due to the difficulty of data collection and privacy concerns that restrict public sharing,synthetic data generation presents a more practical solution to this challenge.This paper proposes a conditional generation method based on a diffusion model,which leverages layer segmentation masks as prior knowledge to guide the generation process.By employing iterative denoising to directly model the pixel space,our approach avoids the precision loss associated with latent diffusion models,thereby producing high-fidelity OCT fingerprint images.Experiments demonstrate that the generated samples exhibit realistic skin structural features,with subjective evaluations by 30 domain experts confirming their anatomically accurate structures and authentic pixel distributions.Further validation shows that augmenting training datasets with synthetic data significantly improves the performance of various anti-spoofing models.

关键词

光学相干断层扫描 / 指纹 / 条件生成 / 伪造攻击检测

Key words

optical coherence tomography / fingerprint / conditional generation / presentation attack detection

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基金资助

国家自然科学基金项目(62376251,62076220)资助;浙江省医药卫生科技计划项目(2024KY881)资助.