An integrated dual factor authenticator based on the face data and tokenised random number

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

Research output: Chapter in Book/Report/Conference proceedingChapter

8 Citations (Scopus)

Abstract

This paper proposed a novel integrated dual factor authenticator based on iterated inner products between tokenised pseudo random number and the user specific facial feature, which generated from a well known subspace feature extraction technique- Fisher Discriminant Analysis, and hence produce a set of user specific compact code that coined as BioCode. The BioCode highly tolerant of data captures offsets, with same user facial data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both tokenised random data and user facial feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. This approach has significant functional advantages over solely biometrics ie. zero EER point and clean separation of the genuine and imposter populations, thereby allowing elimination of FARs without suffering from increased occurrence of FRRs.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsDavid Zhang, Anil K. Jain
PublisherSpringer Verlag
Pages117-123
Number of pages7
ISBN (Print)3540221468, 9783540221463
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3072
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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