Ranking-Based Locality Sensitive Hashing-Enabled Cancelable Biometrics: Index-of-Max Hashing

Zhe Jin, Jung Yeon Hwang, Yen Lung Lai, Soohyung Kim, Andrew Beng Jin Teoh

Research output: Contribution to journalArticle

29 Citations (Scopus)

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 enjoys several merits. First, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Second, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Third, 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.

Original languageEnglish
Article number8038818
Pages (from-to)393-407
Number of pages15
JournalIEEE Transactions on Information Forensics and Security
Volume13
Issue number2
DOIs
Publication statusPublished - 2018 Feb

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

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