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

与当前的生物识别技术相比，汗液可以更好地表示具有更辨别特征的人类身份，克服现有系统的限制，例如人口统计差异（例如，女性的准确性较低）和易受欺骗攻击。这项研究的目的是通过更丰富的信号，不仅是空间特征，而且还相关的化学成分，在不使用试剂的情况下由高光谱成像仪捕获来定义人类身份。该提案旨在通过高光谱成像（HSI）分析汗液，创建一种新的人类身份表示，这使得进一步的研究能够探索汗液作为高效，准确和安全的生物识别人类身份验证的解决方案。汗液可以通过结合生物特征的化学特性来提供对身份的细致观察。通过关注汗液作为生物识别方式的HSI视角，该项目建立了更深层次的身份特征，使真实的人与数字表示之间的联系更强，随后，系统处理它不太容易出错，更容易受到欺骗。该项目更广泛的意义和重要性是将化学汗液分析的进展与成像联系起来，为应用于汗液的HSI学习技术的推理奠定基础。 该项目的创新之处包括使用HSI创建汗液代谢物的配置文件，从而创建基于汗液的数字人类身份。为了实现这一目标，该项目的重点是确认代谢物可以从人类指尖分泌的汗液中提取，确认其重现性，并为每种感兴趣的代谢物创建HSI参考。由于感测方法的多样性，通过汗液代谢物的传统光谱学获得的光谱参考不能用作HSI参考。 该研究调查了重要的方面，例如如何获取适当的汗液样本，以及沉积样本和捕获过程是否可重复-应用HSI。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

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.