Tools for the Generation of Synthetic Biometric Sample Data (GENSYNTH)

生成合成生物特征样本数据的工具 (GENSYNTH)

• 批准号: 421860227
• 负责人: Professorin Dr.-Ing. Jana Dittmann
• 金额: --
• 依托单位: Fachbereich Computerwissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/421860227?language=en
• 关键词: Tools; Generation; Synthetic; Biometric; Sample

Current day biometric recognition and digitized forensics research struggles with a problem severely impeding progress in these security relevant fields: Large scale datasets of biometric data would be required to allow for flexible and timely assessments, but these are missing due to various reasons, amongst them privacy concerns. The latter have increased with the EU GDPR to an extend that even well established standardization bodies like NIST in the USA removed a large part of their publically available datasets before the GDPR became effective in May 2018.To solve this problem and address the attached data quality dimensions (quantitative as well as qualitative concerns), we will research methods allowing for the generation of large-scale sets of plausible and realistic synthetic data to enable reproducible, flexible and timely biometric and forensic experimental assessments, not only compliant with the hunger for data we see with modern day techniques, but also with EU data protection legislation. To achieve our goals, the work in this project follows two distinct solution approaches: The first (data adaptation) takes existing biometric / forensic samples, adapts them to reflect certain acquisition conditions (sensorial, physiological as well as environmental variability), and (if required by the application context) conducts context sensitive control of privacy attributes. The second approach (synthesizing) creates completely artificial samples from scratch according to specified sensorial, physiological as well as environmental variability.The practical work in the project is focused on digitized forensic (latent) fingerprints as well as on the two biometric modalities fingerprint (FP) and vascular data of hand and fingers (i.e. hand- and finger-vein images) (HFV). The theoretical and methodological concepts and empirical findings will be generalized, to discuss the potential benefits of the research performed also for other modalities (esp. in face recognition).

当今的生物识别和数字化取证研究面临着一个严重阻碍这些安全相关领域进展的问题：需要大规模的生物识别数据集来进行灵活和及时的评估，但由于各种原因，这些数据集缺失，其中包括隐私问题。后者随着欧盟GDPR的实施而增加，甚至在2018年5月GDPR生效之前，美国NIST等成熟的标准化机构也删除了大部分可用的数据集。为了解决这个问题并解决附带的数据质量问题，（数量和质量问题），我们将研究允许产生大规模的合理和现实的合成数据集的方法，灵活及时的生物识别和法医实验评估，不仅符合我们在现代技术中看到的对数据的渴望，而且符合欧盟数据保护立法。为了实现我们的目标，该项目中的工作遵循两种不同的解决方案方法：第一种（数据自适应）采用现有的生物识别/法医样本，对其进行调整以反映某些采集条件（感官、生理以及环境变化），并且（如果需要）应用程序上下文）对隐私属性进行上下文敏感控制。第二种方法（合成）根据特定的感官、生理和环境变化从头开始创建完全人工的样本。该项目的实际工作集中在数字化法医（潜在）指纹以及两种生物特征模式指纹（FP）和手部和手指的血管数据（即手部和手指静脉图像）（HFV）。理论和方法的概念和实证研究结果将被概括，讨论研究的潜在好处，也为其他形式（特别是在人脸识别）。