Unifying probabilistic linear discriminant analysis variants in biometric authentication

Abstract

Probabilistic linear discriminant analysis (PLDA) is commonly used in biometric authentication. We review three PLDA variants - standard, simplified and two-covariance - and show how they are related. These clarifications are important because the variants were introduced in literature without argumenting their benefits. We analyse their predictive power, covariance structure and provide scalable algorithms for straightforward implementation of all the three variants. Experiments involve state-of-the-art speaker verification with i-vector features. © 2014 Springer-Verlag Berlin Heidelberg.