  Being accurate, efficient, and compact is essential to a facial landmark
detector for practical use. To simultaneously consider the three concerns, this
paper investigates a neat model with promising detection accuracy under wild
environments e.g., unconstrained pose, expression, lighting, and occlusion
conditions) and super real-time speed on a mobile device. More concretely, we
customize an end-to-end single stage network associated with acceleration
techniques. During the training phase, for each sample, rotation information is
estimated for geometrically regularizing landmark localization, which is then
NOT involved in the testing phase. A novel loss is designed to, besides
considering the geometrical regularization, mitigate the issue of data
imbalance by adjusting weights of samples to different states, such as large
pose, extreme lighting, and occlusion, in the training set. Extensive
experiments are conducted to demonstrate the efficacy of our design and reveal
its superior performance over state-of-the-art alternatives on widely-adopted
challenging benchmarks, i.e., 300W (including iBUG, LFPW, AFW, HELEN, and
XM2VTS) and AFLW. Our model can be merely 2.1Mb of size and reach over 140 fps
per face on a mobile phone (Qualcomm ARM 845 processor) with high precision,
making it attractive for large-scale or real-time applications. We have made
our practical system based on PFLD 0.25X model publicly available at
\url{http://sites.google.com/view/xjguo/fld} for encouraging comparisons and
improvements from the community.
