1. A method for localizing parts of an object in an input image, comprising: training a plurality of local detectors using a plurality of image exemplars as training images, wherein each image exemplar is labeled with a plurality of fiducial points, and wherein each local detector generates a detector score when applied at a location in a training image corresponding to a likelihood that a desired part is located at the location within the training image; generating a plurality of non-parametric global models using at least a portion of the plurality of image exemplars; inputting data corresponding to the input image; applying the trained local detectors to the input image to generate detector scores for the input image; deriving a Bayesian objective function from the plurality of non-parametric global models and the detector scores for the input image using an assumption that locations of fiducial points within the input image are represented within its corresponding global model as hidden variables; optimizing the Bayesian objective function to obtain a consensus set of global models for the hidden variables that best fits the data corresponding to the input image; and generating an output comprising locations of the fiducial points within the object in the input image.