  Face recognition performance improves rapidly with the recent deep learning
technique developing and underlying large training dataset accumulating. In
this paper, we report our observations on how big data impacts the recognition
performance. According to these observations, we build our Megvii Face
Recognition System, which achieves 99.50% accuracy on the LFW benchmark,
outperforming the previous state-of-the-art. Furthermore, we report the
performance in a real-world security certification scenario. There still exists
a clear gap between machine recognition and human performance. We summarize our
experiments and present three challenges lying ahead in recent face
recognition. And we indicate several possible solutions towards these
challenges. We hope our work will stimulate the community's discussion of the
difference between research benchmark and real-world applications.
