SaTC: CORE: Medium: Collaborative: Presentation-attack-robust biometrics systems via computational imaging of physiology and materials

许多身体特征，如面部、指纹和虹膜，以及行为特征，如声音、步态和击键动力学，都被认为是独一无二的。因此，生物特征分析为身份验证问题提供了可靠的解决方案。现在人们普遍认为，生物识别系统很容易受到操纵，其中真正的生物识别是伪造使用各种攻击策略；这种攻击称为表示攻击（Presentation attacks, pa）。该项目开发基于计算成像的方法，用于检测面部和虹膜生物识别系统的已知和未知pa。三十多年来，生物识别技术一直是一个活跃的研究领域，但对各种已知和未知pa具有鲁棒性的生物识别系统的设计仍处于起步阶段。该项目旨在建立集成计算成像传感器的生物识别系统，用于获取生理、光谱和材料特性，目的是检测和减轻已知和未知PA攻击的影响。具体来说，作为该项目的一部分，新型计算相机将通过增加子系统来增强用于面部和虹膜识别的传统传感器，这些子系统可以测量受试者的生理变化，包括生命体征、血液灌注、对外部刺激的自愿和非自愿反应，以及通过散射、反射率和光谱剖面测量的成分。这些计算相机所感知到的丰富特征有望提供真实生物特征及其变化的强大特征，同时为常用的pa提供高可辨别性。此外，借助离群值检测和开集建模，该项目开发了一种高度灵活的方法来检测以前未见过的pa。该奖项反映了美国国家科学基金会的法定使命，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，被认为值得支持。

Many physical characteristics, such as face, fingerprints, and iris as well as behavioral characteristics such as voice, gait, and keystroke dynamics, are believed to be unique to an individual. Hence, biometric analysis offers a reliable solution to the problem of identity verification. It is now widely acknowledged that biometric systems are vulnerable to manipulation where the true biometric is falsified using various attack strategies; such attacks are referred to as Presentation Attacks (PAs). This project develops computational imaging-based methods for detecting known and unknown PAs for face and iris biometric systems.While biometrics has been an active field of research for more than three decades, the design of biometric systems that are robust to a diverse set of known and unknown PAs is still in its infancy. This project seeks to build biometric systems that integrate computational imaging sensors for acquiring physiological, spectral, and material properties with the goal of detecting and mitigating the effects of known and unknown PA attacks. Specifically, as part of the project novel computational cameras will be built that augment traditional sensors used for face and iris recognition by adding subsystems that measure changes in a subject's physiology including vital signs, blood perfusion, voluntary and involuntary responses to external stimuli as well as composition as measured in scattering, reflectance and spectral profiles. The rich signatures sensed by these computational cameras is expected to provide a robust characterization of the true biometric and its variations while providing high discriminability to commonly used PAs. Further, by virtue of outlier detection and open-set modeling, this project develops a highly flexible approach for detecting previously unseen PAs.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.