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

TL;DR

This paper demonstrates that leveraging billions of faces with advanced 3D modeling and discriminative techniques significantly improves unconstrained face recognition performance, especially at zero false positive rate, surpassing previous state-of-the-art results.

Contribution

The paper introduces a face recognition system that uses billions of faces and novel components like 3D shape modeling to achieve unprecedented accuracy without tuning.

Findings

Recall rate nearly doubles at zero false positive matches.

System surpasses previous state-of-the-art results.

Identifies challenging queries for future research.

Abstract

We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive matches, where the obtained recall rate almost doubles the best reported result to date. We discuss the various components and innovations of our system that enable this significant performance gap. These components include extensive utilization of an accurate 3D reconstructed shape model dealing with challenges arising from pose and illumination. In addition, discriminative models based on billions of faces are used in order to overcome aging and facial expression as well as low light and overexposure. Finally, we identify a challenging set of identification queries that might provide useful focus for future research.