1. An image processing method for use in fitting a deformable shape model to an acquired image, wherein the deformable shape model specifies a set of target points whose motion is governed by the model, said method comprising: providing a trained deformable shape model specifying a set of target points; for each target point specified by the trained deformable shape model, generating a corresponding response image from the acquired image by: providing a feature detector to locate the target point within the acquired image, wherein said feature detector includes a random forest comprising one or more decision trees; scanning a patch image across the acquired image to define multiple sampling locations with respect to the acquired image; for each sampling location of the patch image with respect to the acquired image, performing regression voting using the random forest to produce one or more votes for the location of the target point within the acquired image; and accumulating the regression votes for all of the multiple sampling locations to generate said response image corresponding to the target point, wherein the response image is a mapping showing, for each position in the response image, the probability or cost of the corresponding target point being located at that point; and performing an optimization and thereby determining positions within the acquired image for the set of target points, wherein the optimization uses the corresponding response images and is subject to constraint by the deformable shape model.