Large Scale Learning of Active Shape Models

We propose a framework to learn statistical shape models for faces as piecewise linear models. Specifically, our methodology builds upon primitive active shape models(ASM) to handle large scale variation in shapes and appearances of faces. Non-linearities in shape manifold arising due to large head rotation cannot be accurately modeled using ASM. Moreover overly general image descriptor causes the cost function to have multiple local minima which in turn degrades the quality of shape registration. We propose to use multiple overlapping subspaces with more discriminative local image descriptors to capture larger variance occurring in the data set. We also apply techniques to learn distance metric for enhancing similarity of descriptors belonging to the same class of shape subspace. Our generic algorithm can be applied to large scale shape analysis and registration.