  Although deep neural networks have achieved reasonable accuracy in solving
face alignment, it is still a challenging task, specifically when we deal with
facial images, under occlusion, or extreme head poses. Heatmap-based Regression
(HBR) and Coordinate-based Regression (CBR) are among the two mainly used
methods for face alignment. CBR methods require less computer memory, though
their performance is less than HBR methods. In this paper, we propose an
Adaptive Coordinate-based Regression (ACR) loss to improve the accuracy of CBR
for face alignment. Inspired by the Active Shape Model (ASM), we generate
Smooth-Face objects, a set of facial landmark points with less variations
compared to the ground truth landmark points. We then introduce a method to
estimate the level of difficulty in predicting each landmark point for the
network by comparing the distribution of the ground truth landmark points and
the corresponding Smooth-Face objects. Our proposed ACR Loss can adaptively
modify its curvature and the influence of the loss based on the difficulty
level of predicting each landmark point in a face. Accordingly, the ACR Loss
guides the network toward challenging points than easier points, which improves
the accuracy of the face alignment task. Our extensive evaluation shows the
capabilities of the proposed ACR Loss in predicting facial landmark points in
various facial images.
