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

许多生理特征，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，被认为是个人独有的。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，生物特征识别系统容易受到操纵，其中真实的生物特征识别是使用各种攻击策略来篡改的；这种攻击被称为呈现攻击(PA)。该项目开发了基于计算成像的人脸和虹膜生物识别系统的已知和未知PA的检测方法。虽然生物识别已经是一个活跃的研究领域超过30年，但设计出对各种已知和未知的PA具有健壮性的生物识别系统仍处于起步阶段。该项目旨在构建生物识别系统，集成计算成像传感器以获取生理、光谱和材料属性，目标是检测和减轻已知和未知PA攻击的影响。具体地说，作为该项目的一部分，将建造新型计算相机，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生理上的变化，包括生命体征、血液灌流、对外部刺激的自愿和非自愿反应，以及通过散射、反射和光谱曲线测量的成分。这些计算摄像机检测到的丰富的签名有望提供对真实生物特征及其变化的稳健描述，同时提供对常用PA的高区分性。此外，凭借离群值检测和开放集合建模，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NSF的法定使命，并通过使用基金会的智力优势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.