  Recently, heatmap regression models have become the mainstream in locating
facial landmarks. To keep computation affordable and reduce memory usage, the
whole procedure involves downsampling from the raw image to the output heatmap.
However, how much impact will the quantization error introduced by downsampling
bring? The problem is hardly systematically investigated among previous works.
This work fills the blank and we are the first to quantitatively analyze the
negative gain. The statistical results show the NME generated by quantization
error is even larger than 1/3 of the SOTA item, which is a serious obstacle for
making a new breakthrough in face alignment. To compensate the impact of
quantization effect, we propose a novel method, called Heatmap In Heatmap(HIH),
which leverages two categories of heatmaps as label representation to encode
coordinate. And in HIH, the range of one heatmap represents a pixel of the
other category of heatmap. Also, we even combine the face alignment with
solutions of other fields to make a comparison. Extensive experiments on
various benchmarks show the feasibility of HIH and the superior performance
than other solutions. Moreover, the mean error reaches to 4.18 on WFLW, which
exceeds SOTA a lot. Our source code are made publicly available at
supplementary material.
