Non-user-specific multivariate biometric discretization with medoid-based segmentation

Meng Hui Lim, Andrew Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Univariate discretization approach that transforms continuous attributes into discrete elements/binary string based on discrete/binary feature extraction on a single dimensional basis have been attracting much attention in the biometric community mainly to derive biometric-based cryptographic key derivation for security purpose. However, since components of biometric feature are interdependent, univariate approach may destroy important interactions with such attributes and thus very likely to cause features being discretized suboptimally. In this paper, we introduce a multivariate discretization approach encompassing a medoid-based segmentation with effective segmentation encoding technique. Promising empirical results on two benchmark face datasets significantly justify the superiority of our approach with reference to several non-user-specific univariate biometric discretization schemes.

Original languageEnglish
Title of host publicationBiometric Recognition - 6th Chinese Conference, CCBR 2011, Proceedings
Number of pages9
Publication statusPublished - 2011
Event6th Chinese Conference on Biometric Recognition, CCBR 2011 - Beijing, China
Duration: 2011 Dec 32011 Dec 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7098 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th Chinese Conference on Biometric Recognition, CCBR 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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