Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing
Zhe Jin, Yen-Lung Lai, Jung-Yeon Hwang, Soohyung Kim, Andrew Beng Jin, Teoh

TL;DR
This paper introduces Index-of-Max (IoM) hashing, a novel ranking-based locality sensitive hashing method for cancelable biometric templates, enhancing security, robustness, and privacy in fingerprint recognition systems.
Contribution
It proposes a new IoM hashing scheme with two realizations, improving biometric template protection through non-invertibility, robustness, and scale-invariance.
Findings
Achieves high accuracy on FVC2002 and FVC2004 fingerprint datasets.
Demonstrates resilience against security and privacy attacks.
Satisfies revocability and unlinkability criteria for cancelable biometrics.
Abstract
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…
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