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: Contribution to journalArticle

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
Pages (from-to)117-123
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3072
Publication statusPublished - 2004 Dec 1

Fingerprint

Random number
Biometrics
Face
Discriminant analysis
Feature extraction
Data acquisition
Fabrication
Fisher Discriminant Analysis
Pseudorandom numbers
Zero Point
Scalar, inner or dot product
Feature Extraction
Elimination
Subspace

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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