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Heatmap-based regression overcomes the lack of spatial and contextualinformation of direct coordinate regression, and has revolutionized the task offace alignment. Yet it suffers from quantization errors caused by neglectingsubpixel coordinates in image resizing and network downsampling. In this paper,we first quantitatively analyze the quantization error on benchmarks, whichaccounts for more than 1/3 of the whole prediction errors for state-of-the-artmethods. To tackle this problem, we propose a novel Heatmap In Heatmap(HIH)representation and a coordinate soft-classification (CSC) method, which areseamlessly integrated into the classic hourglass network. The HIHrepresentation utilizes nested heatmaps to jointly represent the coordinatelabel: one heatmap called integer heatmap stands for the integer coordinate,and the other heatmap named decimal heatmap represents the subpixel coordinate.The range of a decimal heatmap makes up one pixel in the corresponding integerheatmap. Besides, we transfer the offset regression problem to an intervalclassification task, and CSC regards the confidence of the pixel as theprobability of the interval. Meanwhile, CSC applying the distribution lossleverage the soft labels generated from the Gaussian distribution function toguide the offset heatmap training, which makes it easier to learn thedistribution of coordinate offsets. Extensive experiments on challengingbenchmark datasets demonstrate that our HIH can achieve state-of-the-artresults. In particular, our HIH reaches 4.08 NME (Normalized Mean Error) onWFLW, and 3.21 on COFW, which exceeds previous methods by a significant margin.

HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap