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