Missing or degraded information continues to be a significant practical challenge facing automatic face representation and recognition. Generally, existing approaches seek either to generatively invert the degradation process or find discriminative representations that are immune to it. Ideally, the solution to this problem exists between these two perspectives. To this end, in this paper we show the efficacy of using probabilistic linear subspace modes (in particular, variational probabilistic PCA) for both modeling and recognizing facial data under disguise or occlusion. From a discriminative perspective, we verify the efficacy of this approach for attenuating the effect of missing data due to disguise and non-linear speculars in several verification experiments. From a generative view, we show its usefulness in not only estimating missing information but also understanding facial covariates for image reconstruction. In addition, we present a least-squares connection to the maximum likelihood solution under missing data and show its intuitive connection to the geometry of the subspace learning problem.