1. A method for localizing parts of an object in an input image, comprising using a computer processor to perform the steps of: 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, wherein each non-parametric global model Xk,t comprises locations of parts in an image exemplar k transformed by a similarity transform t; receiving data corresponding to the input image; applying the trained local detectors to the input image to generate detector scores D for the input image; deriving a Bayesian objective function from the plurality of non-parametric global models Xk,t and the detector scores D using an assumption that locations of fiducial points within the input image are represented within its a potentially matching non-parametric global model as hidden variables comprising the image exemplar k and the similarity transform t corresponding to that non-parametric global model; optimizing the Bayesian objective function to obtain a consensus set of non-parametric global models Xk,t for the hidden variables that best fits the detector scores D corresponding to the input image; and using the consensus set, generating an output comprising estimated locations of the fiducial points within the object in the input image.