  Although heatmap regression is considered a state-of-the-art method to locate
facial landmarks, it suffers from huge spatial complexity and is prone to
quantization error. To address this, we propose a novel attentive
one-dimensional heatmap regression method for facial landmark localization.
First, we predict two groups of 1D heatmaps to represent the marginal
distributions of the x and y coordinates. These 1D heatmaps reduce spatial
complexity significantly compared to current heatmap regression methods, which
use 2D heatmaps to represent the joint distributions of x and y coordinates.
With much lower spatial complexity, the proposed method can output
high-resolution 1D heatmaps despite limited GPU memory, significantly
alleviating the quantization error. Second, a co-attention mechanism is adopted
to model the inherent spatial patterns existing in x and y coordinates, and
therefore the joint distributions on the x and y axes are also captured. Third,
based on the 1D heatmap structures, we propose a facial landmark detector
capturing spatial patterns for landmark detection on an image; and a tracker
further capturing temporal patterns with a temporal refinement mechanism for
landmark tracking. Experimental results on four benchmark databases demonstrate
the superiority of our method.
