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: for each target point, 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, wherein different trees in each random forest are trained on different, randomly selected subsets of an overall training data set; 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; and performing an optimization to determine positions for the set of target points within the acquired image based on the corresponding response images and subject to constraint by the deformable shape model. for each target point, 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, wherein different trees in each random forest are trained on different, randomly selected subsets of an overall training data set; 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; and 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, wherein different trees in each random forest are trained on different, randomly selected subsets of an overall training data set; 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; and performing an optimization to determine positions for the set of target points within the acquired image based on the corresponding response images and subject to constraint by the deformable shape model.