EAGER: SaTC: Sweaty Digits: Bridging Chemistry and AI-Empowered Imaging for Secure and Trustworthy Human Identity Verification

EAGER：SaTC：汗水数字：桥接化学和人工智能成像，实现安全可信的人类身份验证

• 批准号: 2330240
• 负责人: Emanuela Marasco
• 金额: $ 20万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330240&HistoricalAwards=false
• 关键词: EAGER; SaTC; Sweaty; Digits; Bridging

Compared to current biometric technologies, sweat can better represent human identity with more discerning characteristics, overcoming limitations of existing systems such as demographic differentials (e.g., lower accuracy in women) and vulnerability to spoof attacks. This research aims to define human identity through richer signals, not only spatial features but also associated chemical content, captured by a hyperspectral imager without the use of reagents. This proposal aims to create a new representation of human identity based on the analysis of sweat through hyperspectral imaging (HSI), which enables further research to explore sweat as a solution for efficient, accurate, and secure biometric human identity verification. Sweat can provide a meticulous perspective on identity by incorporating chemical properties of a biometric trait. By focusing on an HSI perspective of sweat as biometric modality, this project builds a deeper profile of the identity makes the link between the genuine person and the digital representation stronger and, subsequently, the system processing it less prone to errors and more resilient to spoofing. The project's broader significance and importance is to bridge advances in chemical sweat analysis to imaging that builds foundations for reasoning on HSI learning techniques applied to sweat. The project’s novelties include creating profiles of sweat metabolites using HSI, thereby creating a digital human identity based on sweat. To accomplish this objective, this project focuses on confirming that metabolites can be extracted from sweat excreted from human fingertips, confirming their reproducibility, and creating an HSI reference for each metabolite of interest. Due to diversity in the sensing approach, spectral references obtained through traditional spectroscopy for sweat metabolites cannot be used as HSI reference. The research investigates important aspects such as how to acquire appropriate sweat samples and whether the deposited sample and the capture process are repeatable - with the application of HSI.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

与当前的生物特征识别技术相比，汗水可以更好地代表具有更具敏锐特征的人类身份，克服了现有系统的局限性，例如人口统计学差异（例如，女性的准确性降低）以及对欺骗攻击的脆弱性。这项研究旨在通过更丰富的信号来定义人类的身份，不仅是空间特征，而且还与相关的化学含量来定义，这是由高光谱成像仪捕获的，而无需使用试剂。基于通过高光谱成像（HSI）对汗液分析的人类身份的新表示，这使得进一步的研究能够探索汗水作为有效，准确和安全的生物特征识别人类认同验证的解决方案。汗水可以通过结合生物特征性状的化学特性来提供有关身份的细致观点。通过关注汗水作为生物识别模式的HSI观点，该项目对身份的更深刻的了解使真实的人与数字表示之间的联系更加强大，随后，系统处理的系统较不容易出现错误，并且更有弹性。该项目的更广泛的意义和重要性是弥合化学汗水分析的进步，以便想象为应用于汗水的HSI学习技术的推理而建立基础。该项目的新颖性包括使用HSI创建汗水代谢产物的概况，从而基于汗水创建了数字人类身份。为了实现这一目标，该项目的重点是确认可以从人类指尖超越汗水中提取代谢产物，确认其可重复性，并为每个感兴趣的代谢物创建HSI参考。由于灵敏度方法中的多样性，通过传统光谱进行汗液代谢物获得的光谱参考不能用作HSI参考。该研究调查了重要方面，例如如何获取适当的汗水样本，以及是否可以重复存入样本和捕获过程 - 使用HSI。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准通过评估来获得的支持。

项目成果

Vision Paper: Hyperspectral Analysis of Finger Skin Reflectance for Resilient Biometric Systems

• DOI: 10.1109/bigdata59044.2023.10386372
• 发表时间: 2023-12
• 期刊: 2023 IEEE International Conference on Big Data (BigData)
• 作者: Emanuela Marasco