  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 is introducing a new idea to
exploit as a transformation function for cancelable biometrics aimed at
protecting the 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 against leakage attacks while improving the recognition
performance.
