  In this paper, we propose a ranking based locality sensitive hashing inspired
two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for
biometric template protection. With externally generated random parameters, IoM
hashing transforms a real-valued biometric feature vector into discrete index
(max ranked) hashed code. We demonstrate two realizations from IoM hashing
notion, namely Gaussian Random Projection based and Uniformly Random
Permutation based hashing schemes. The discrete indices representation nature
of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empowers strong
concealment to the biometric information. This contributes to the solid ground
of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the
features magnitude, hence is more robust against biometric features variation.
Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes
being scale-invariant, which is critical for matching and feature alignment.
The experimental results demonstrate favorable accuracy performance on
benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its
resilience to the existing and newly introduced security and privacy attacks as
well as satisfy the revocability and unlinkability criteria of cancelable
biometrics.
