This work presents a novel non-parametric Bayesian formulation for aligning faces in unseen images. Popular approaches, such as the Constrained Local Models (CLM) or the Active Shape Models (ASM), perform facial alignment through a local search, combining an ensemble of detectors with a global optimization strategy that constraints the facial feature points to be within the subspace spanned by a Point Distribution Model (PDM). The global optimization can be posed as a Bayesian inference problem, looking to maximize the posterior distribution of the PDM parameters in a maximum a posteriori (MAP) sense. Previous approaches rely exclusively on Gaussian inference techniques, i.e. both the likelihood (detectors responses) and the prior (PDM) are Gaussians, resulting in a posterior which is also Gaussian, whereas in this work the posterior distribution is modeled as being non-parametric by a Kernel Density Estimator (KDE). We show that this posterior distribution can be efficiently inferred using Sequential Monte Carlo methods, in particular using a Regularized Particle Filter (RPF). The technique is evaluated in detail on several standard datasets (IMM, BioID, XM2VTS, LFW and FGNET Talking Face) and compared against state-of-the-art CLM methods. We demonstrate that inferring the PDM parameters non-parametrically significantly increase the face alignment performance.