  Current fully-supervised facial landmark detection methods have progressed
rapidly and achieved remarkable performance. However, they still suffer when
coping with faces under large poses and heavy occlusions for inaccurate facial
shape constraints and insufficient labeled training samples. In this paper, we
propose a semi-supervised framework, i.e., a Self-Calibrated Pose Attention
Network (SCPAN) to achieve more robust and precise facial landmark detection in
challenging scenarios. To be specific, a Boundary-Aware Landmark Intensity
(BALI) field is proposed to model more effective facial shape constraints by
fusing boundary and landmark intensity field information. Moreover, a
Self-Calibrated Pose Attention (SCPA) model is designed to provide a
self-learned objective function that enforces intermediate supervision without
label information by introducing a self-calibrated mechanism and a pose
attention mask. We show that by integrating the BALI fields and SCPA model into
a novel self-calibrated pose attention network, more facial prior knowledge can
be learned and the detection accuracy and robustness of our method for faces
with large poses and heavy occlusions have been improved. The experimental
results obtained for challenging benchmark datasets demonstrate that our
approach outperforms state-of-the-art methods in the literature.
