  In facial landmark localization tasks, various occlusions heavily degrade the
localization accuracy due to the partial observability of facial features. This
paper proposes a structural relation network (SRN) for occlusion-robust
landmark localization. Unlike most existing methods that simply exploit the
shape constraint, the proposed SRN aims to capture the structural relations
among different facial components. These relations can be considered a more
powerful shape constraint against occlusion. To achieve this, a hierarchical
structural relation module (HSRM) is designed to hierarchically reason the
structural relations that represent both long- and short-distance spatial
dependencies. Compared with existing network architectures, HSRM can
efficiently model the spatial relations by leveraging its geometry-aware
network architecture, which reduces the semantic ambiguity caused by occlusion.
Moreover, the SRN augments the training data by synthesizing occluded faces. To
further extend our SRN for occluded video data, we formulate the occluded face
synthesis as a Markov decision process (MDP). Specifically, it plans the
movement of the dynamic occlusion based on an accumulated reward associated
with the performance degradation of the pre-trained SRN. This procedure
augments hard samples for robust facial landmark tracking. Extensive
experimental results indicate that the proposed method achieves outstanding
performance on occluded and masked faces. Code is available at
https://github.com/zhuccly/SRN.
