Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in b...Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems.This transformation is repeatable enabling subsequent biometric comparisons.This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks.Our proposed scheme is based on time-varying keys(random biometrics in our case)and morphing transformations.An experimental implementation of the proposed scheme is given for face biometrics.The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.展开更多
基金This work was supported by PRIMA(No.H2020-MSCA-ITN-2019-860315)TRESPASS-ETN(No.H2020-MSCA-ITN-2019-860813)+1 种基金BBforTAI(No.PID2021-127641OB-I00 MICINN/FEDER)INTER-ACTION(No.PID2021-126521OB-I00 MICINN/FEDER).M.Ghafourian was supported by PRIMA and I.Serna was supported by an FPI fellowship from University Autonoma de Madrid,Spain.
文摘Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems.This transformation is repeatable enabling subsequent biometric comparisons.This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks.Our proposed scheme is based on time-varying keys(random biometrics in our case)and morphing transformations.An experimental implementation of the proposed scheme is given for face biometrics.The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.
