基于深度生成模型合成虹膜和指纹图像的生物特征数据可靠性测试框架

IF 3.6 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-09-02 DOI:10.1109/ACCESS.2025.3604894

Hyoungrae Kim;Hakil Kim

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引用次数: 0

摘要

本文提出了一个全面的数据可靠性测试框架，用于评估合成生物特征数据，解决指纹和虹膜识别系统中的隐私问题。这种统一的、独立于模态的方法建立了六个定量指标：随机性、质量相似性、属性相似性、非重复、id保存和几何多样性。该框架通过一种新颖的RD-Net体系结构实现，该体系结构由用于隐私保护的随机网络和用于维持基本生物特征的确定性网络组成。使用公共数据集（FVC 2002、IITDelhi-Iris和CASIA-Iris-V4）进行的实验表明，合成样本在保持其结构特性的同时保持了与源数据集的高度不相似性。通过提出的随机网络和确定性网络架构生成的合成生物特征数据使用数据可靠性测试框架进行评估，确认所有提议指标与真实数据的分布相似性，并获得超过80分。该方法提供了一种生成和评估合成生物识别数据的方法，该方法在生物识别系统开发和测试中平衡了隐私保护与功能有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Data Reliability Testing Framework for Biometric Datasets Using Synthetic Iris and Fingerprint Images Generated via Deep Generative Models

This paper presents a comprehensive data reliability testing framework for evaluating synthetic biometric data, addressing privacy concerns in fingerprint and iris recognition systems. This unified and modality-independent methodology establishes six quantitative metrics: randomness, quality similarity, attribute similarity, non-duplication, ID-preservation, and geometric diversity. The framework is implemented through a novel RD-Net architecture consisting of a Random Network for privacy protection and a Deterministic Network for maintaining essential biometric characteristics. Experiments using public datasets (FVC 2002, IITDelhi-Iris, and CASIA-Iris-V4) demonstrate that synthetic samples maintain high dissimilarity from source datasets while preserving their structural properties. The synthetic biometric data generated through the proposed Random Network and Deterministic Network architectures are evaluated using the data reliability testing framework, confirming distribution similarity with real data across all proposed metrics and achieving scores over 80. This approach offers a method for generating and evaluating synthetic biometric data that balances privacy protection with functional validity in biometric system development and testing.

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.