This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression-and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a highresolution 3D surface model.
Breuer et al. REF use Support Vector Machines to detect the face in a 2D image and then extract facial features using a regression-and classification-based approach.
Automatic 3D face reconstruction from single images or video
2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
Computer Science