  Accurate face landmark localization is an essential part of face recognition,
reconstruction and morphing. To accurately localize face landmarks, we present
our heatmap regression approach. Each model consists of a MobileNetV2 backbone
followed by several upscaling layers, with different tricks to optimize both
performance and inference cost. We use five na\"ive face landmarks from a
publicly available face detector to position and align the face instead of
using the bounding box like traditional methods. Moreover, we show by adding
random rotation, displacement and scaling -- after alignment -- that the model
is more sensitive to the face position than orientation. We also show that it
is possible to reduce the upscaling complexity by using a mixture of
deconvolution and pixel-shuffle layers without impeding localization
performance. We present our state-of-the-art face landmark localization model
(ranking second on The 2nd Grand Challenge of 106-Point Facial Landmark
Localization validation set). Finally, we test the effect on face recognition
using these landmarks, using a publicly available model and benchmarks.
