3D facial model synthesis using coupled dictionaries

In this work, we propose a generative way of modeling faces, where the 3D shape of a face is generated by a supervised learning procedure involving coupled sparse feature learning. To learn dictionaries using the proposed method, we use the USF-HUMAN ID database [1]. We provide as input to our training system, paired correspondences of 2D and 3D images of individuals and aim to learn the low-level patches both in 2D and 3D domains that describe the corresponding subspaces in a sparse manner. We demonstrate the efficacy of our method by quantitative results on the 3D database and qualitative results on images drawn from the internet.