In this work, an accurate method to register multi-view images of the human torso is developed. In particular, a new framework that incorporates prior statistical knowledge about the registration is developed and tested. This framework leads to a computationally efficient procedure to accurately align images of the human torso. An intensity based image registration procedure is used to obtain the deformation fields by modelling them as both locally affine and globally smooth. Next, the estimated geometric deformation fields are analyzed in order to construct a prior deformation model. Two subspace analysis projection techniques are used to construct the subspaces of plausible deformations, namely principal component analysis (PCA) and independent component analysis (ICA). Accurate deformations are now guaranteed by projecting the locally computed geometric transformations onto the subspaces of plausible deformations. The proposed registration method was validated using high resolution images of the human torso. In order to handle the high resolution images, a multi-resolution framework was employed in the registration process. Experiments demonstrate promising performance in terms of mean square error and in the computational complexity. The main contribution of this work is the development of image registration method that uses subspace constraints to align images of the human torso. This method did not use the intra and inter image constraints used in most intensity based image registration algorithms in the literature.