Classification Performance Comparison of a Continuous and Binary Classifier under Gaussian Assumption
Emile J. C. Kelkboom, Jasper Goseling, Jos H. Weber, Jeroen Breebaart, Raymond N. J. Veldhuis · University of Twente Research Information · 2010
Template protection techniques are privacy and security enhancing techniques of bio-metric reference data within a biometric system. Several of the template protection schemes known in the literature require the extraction of a binary representation from the real-valued biometric sample, which raises the question whether the bit extraction method reduces the classification performance. In this work we provide the theoreti-cal performance of the optimal log likelihood ratio continuous classifier and compare it with the theoretical performance of a binary Hamming distance classifier with a single bit extraction scheme as known from the literature. We assume biometric data mod-eled by a Gaussian between-class and within-class probability density with independent feature components and we also include the effect of averaging multiple enrolment and verification samples. 1