Deformable Face Fitting with Soft Correspondence Constraints

Despite significant progress in deformable model fitting over the last decade, the problem of efficient and accurate person-independent face fitting remains a challenging problem. In this work, a reformulation of the generative fitting objective is presented, where only soft correspondences between the model and the image are enforced. This has the dual effect of improving robustness to unseen faces as well as affording fitting time which scales linearly with the model's complexity. This approach is compared with three state-of-the-art fitting methods on the problem of person independent face fitting, where it is shown to closely approach the accuracy of the currently best performing method while affording significant computational savings.