TY - GEN
T1 - Non-user-specific multivariate biometric discretization with medoid-based segmentation
AU - Lim, Meng Hui
AU - Teoh, Andrew Beng Jin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=81155151835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=81155151835&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25449-9_35
DO - 10.1007/978-3-642-25449-9_35
M3 - Conference contribution
AN - SCOPUS:81155151835
SN - 9783642254482
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 279
EP - 287
BT - Biometric Recognition - 6th Chinese Conference, CCBR 2011, Proceedings
T2 - 6th Chinese Conference on Biometric Recognition, CCBR 2011
Y2 - 3 December 2011 through 4 December 2011
ER -