A multiview face identification model with no geometric constraints

Face identification systems relying on local descriptors are increasingly used because of their perceived robustness with respect to occlusions and to global geometrical deformations. Descriptors of this type - based on a set of oriented Gaussian derivative filters - are used in our identification system. In this paper, we explore a pose-invariant multiview face identification system that does not use explicit geometrical information. The basic idea of the approach is to find discriminant features to describe a face across different views. A boosting procedure is used to select features out of a large feature pool of local features collected from the positive training examples. We describe experiments on well-known, though small, face databases with excellent recognition rate