CAREER: Human-Machine Supervision Cycle for Trustworthy Biometrics

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Dominant approaches of biometric attack detection make strong assumptions about the type of deviations from authentic information. This creates a critical gap between reliability observed in laboratory settings and the performance expected in the real world, where future attacks are unknown. This project fills this gap and builds an effective symbiosis between Artificial Intelligence (AI) and humans. The project novelties are new methods that (a) allow the AI to effectively learn from humans how to increase detectability of unknown attacks on biometric systems, and (b) support humans in their examination of fake biometric inputs. The project's broader significance and importance are: (a) trustworthy biometric systems that better recognize never-seen presentation attacks, and thus better protect consumer devices, bank accounts and strengthen the US border control processes; (b) a strong educational program that exposes K-12, undergraduate and graduate students to both the security- and ethics-related aspects of biometrics, and broadens their knowledge in a relevant topic of national concern; (c) publicly available lectures prepared by the investigator, which will broaden the awareness of responsible use of biometrics.In this project, a holistic framework for human-machine supervision cycle will be established to enable (a) human-guided design of computer vision methods to make the biometric presentation attack detection mechanisms generalize better to unknown attack instruments and (b) creation of computer-aided methods of assisting human examiners in detecting of fake inputs. Fundamental technical contributions of this project include (1) broadening knowledge about mechanisms that govern human perception of fake visual signals, (2) discovering the most effective human-interpretable representations of information to support their decisions and speed up their learning of new types of attacks, (3) developing quantitative metrics of trust assessment and linking human and machine decisions into a trustworthy tandem that makes better judgements on the authenticity of biometric inputs, and (4) application of the framework to iris recognition of newborns, contributing to a better linkage with mothers and/or guardians, resulting in improved chances to benefit from healthcare systems, especially in developing countries.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.