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

Authors:
Meng-Hui Lim;Andrew Beng Jin Teoh
Affiliations:
School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea;School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea
Venue:
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Year:
2011

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Abstract

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.