Multi-view face alignment using direct appearance models

Alignment makes face distribution statistically more compact than un-aligned faces and provides a good basis for face modeling, recognition and synthesis. In this paper we present a method for multi-view face alignment using a new model called direct appearance model (DAM). Like active appearance model (AAM), DAM also makes use of both shape and texture constraints; however it does this without combining shape and texture as in AAM. The way that DAM models shapes and textures has the following advantages as compared to AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) the convergence and accuracy are improved, and (3) the memory requirement is cut down to a large extent. Extensive experiments are presented to evaluate the DAM alignment in comparison with AAM.