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.
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Lim, MH., Teoh, A.B.J. (2011). Non-user-Specific Multivariate Biometric Discretization with Medoid-Based Segmentation. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds) Biometric Recognition. CCBR 2011. Lecture Notes in Computer Science, vol 7098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25449-9_35
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DOI: https://doi.org/10.1007/978-3-642-25449-9_35
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