We describe an expression-invariant method for face recognition by fitting an identity/expression separated 3D Morphable Model to shape data. The expression model greatly improves recognition and retrieval rates in the uncooperative setting, while achieving recognition rates on par with the best recognition algorithms in the face recognition great vendor test. The fitting is performed with a robust nonrigid ICP algorithm. It is able to perform face recognition in a fully automated scenario and on noisy data. The system was evaluated on two datasets, one with a high noise level and strong expressions, and the standard UND range scan database, showing that while expression invariance increases recognition and retrieval performance for the expression dataset, it does not decrease performance on the neutral dataset. The high recognition rates are achieved even with a purely shape based method, without taking image data into account.
Amberg et al. REF introduces an expression-invariant method for face recognition by fitting an identity/expression separated 3D morphable model to shape data.
Expression invariant 3D face recognition with a Morphable Model
2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
2008 8th IEEE International Conference on Automatic Face & Gesture Recognition
Computer Science