Abstract
3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for pure 2D texture images were shown sensitive to lighting condition changes. In this paper, we present a statistical model-based technique for accurate 3D face landmarking, thus using an ¿analysis by synthesis¿ approach. Our model learns from a training set both variations of global face shapes as well as the local ones in terms of scale-free texture and range patches around each landmark. Given a shape instance, local regions of a new face can be approximated by synthesizing texture and range instances using respectively the texture and range models. By optimizing an objective function describing the similarity of the new face and instances, we can optimize the best shape in order to locate the landmarks. Experimented on more than 1860 face models from FRGC datasets, our method achieves an average of locating errors less than 7 mm for 15 feature points. Compared with a curvature analysis-based method also developed within our team, this learning-based method enables localization of more facial landmarks with a general better accuracy at the cost of a learning step.