We address the problem of robust facial feature localization in the presence of occlusions, which remains a lingering problem in facial analysis despite intensive long-term studies. Recently, regression-based approaches to localization have produced accurate results in many cases, yet are still subject to significant error when portions of the face are occluded. To overcome this weakness, we propose an occlusion-robust regression method by forming a consensus from estimates arising from a set of occlusion-specific regressors. That is, each regressor is trained to estimate facial feature locations under the precondition that a particular pre-defined region of the face is occluded. The predictions from each regressor are robustly merged using a Bayesian model that models each regressor’s prediction correctness likelihood based on local appearance and consistency with other regressors with overlapping occlusion regions. After localization, the occlusion state for each landmark point is estimated using a Gaussian MRF semi-supervised learning method. Experiments on both non-occluded and occluded face databases demonstrate that our approach achieves consistently better results over state-of-the-art methods for facial landmark localization and occlusion detection.