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We employ the face recognition technology developed in house at face.com to awell accepted benchmark and show that without any tuning we are able toconsiderably surpass state of the art results. Much of the improvement isconcentrated in the high-valued performance point of zero false positivematches, where the obtained recall rate almost doubles the best reported resultto date. We discuss the various components and innovations of our system thatenable this significant performance gap. These components include extensiveutilization of an accurate 3D reconstructed shape model dealing with challengesarising from pose and illumination. In addition, discriminative models based onbillions of faces are used in order to overcome aging and facial expression aswell as low light and overexposure. Finally, we identify a challenging set ofidentification queries that might provide useful focus for future research.

Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition