Random multispace quantization as an analytic mechanism for biohashing of biometric and random identity inputs

Andrew B.J. Teoh, David C.L. Ngo

Research output: Contribution to journalArticlepeer-review

300 Citations (Scopus)

Abstract

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs.

Original languageEnglish
Pages (from-to)1892-1901
Number of pages10
JournalIEEE transactions on pattern analysis and machine intelligence
Volume28
Issue number12
DOIs
Publication statusPublished - 2006 Dec

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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