  Recent face recognition experiments on the LFW benchmark show that face
recognition is performing stunningly well, surpassing human recognition rates.
In this paper, we study face recognition at scale. Specifically, we have
collected from Flickr a \textbf{Million} faces and evaluated state of the art
face recognition algorithms on this dataset. We found that the performance of
algorithms varies--while all perform great on LFW, once evaluated at scale
recognition rates drop drastically for most algorithms. Interestingly, deep
learning based approach by \cite{schroff2015facenet} performs much better, but
still gets less robust at scale. We consider both verification and
identification problems, and evaluate how pose affects recognition at scale.
Moreover, we ran an extensive human study on Mechanical Turk to evaluate human
recognition at scale, and report results. All the photos are creative commons
photos and is released at \small{\url{http://megaface.cs.washington.edu/}} for
research and further experiments.
