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.