  Large scale face recognition is challenging especially when the computational
budget is limited. Given a \textit{flops} upper bound, the key is to find the
optimal neural network architecture and optimization method. In this article,
we briefly introduce the solutions of team 'trojans' for the ICCV19 -
Lightweight Face Recognition Challenge~\cite{lfr}. The challenge requires each
submission to be one single model with the computational budget no higher than
30 GFlops. We introduce a searched network architecture `Efficient PolyFace'
based on the Flops constraint, a novel loss function `ArcNegFace', a novel
frame aggregation method `QAN++', together with a bag of useful tricks in our
implementation (augmentations, regular face, label smoothing, anchor
finetuning, etc.). Our basic model, `Efficient PolyFace', takes 28.25 Gflops
for the `deepglint-large' image-based track, and the `PolyFace+QAN++' solution
takes 24.12 Gflops for the `iQiyi-large' video-based track. These two solutions
achieve 94.198\% @ 1e-8 and 72.981\% @ 1e-4 in the two tracks respectively,
which are the state-of-the-art results.
