In this paper, we propose a novel generative approach for face authentication, based on a Local Binary Pattern (LBP) description of the face. A generic face model is considered as a collection of LBP-histograms. Then, a client-specific model is obtained by an adaptation technique from this generic model under a probabilistic framework. We compare the proposed approach to standard state-of-the-art face authentication methods on two benchmark databases, namely XM2VTS and BANCA, associated to their experimental protocol. We also compare our approach to two state-of-the-art LBP-based face recognition techniques, that we have adapted to the verification task.