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Automatic 3D Face Reconstruction from Single Images or Video
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.
@inproceedings{5237, title = {Automatic 3D Face Reconstruction from Single Images or Video}, journal = {Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008)}, booktitle = {FG 2008}, abstract = {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.}, pages = {1-8}, publisher = {IEEE Computer Society}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Los Alamitos, CA, USA}, month = sep, year = {2008}, slug = {5237}, author = {Breuer, P. and Kim, KI. and Kienzle, W. and Sch{\"o}lkopf, B. and Blanz, V.}, month_numeric = {9} }