  Facial landmark detection aims to localize the anatomically defined points of
human faces. In this paper, we study facial landmark detection from partially
labeled facial images. A typical approach is to (1) train a detector on the
labeled images; (2) generate new training samples using this detector's
prediction as pseudo labels of unlabeled images; (3) retrain the detector on
the labeled samples and partial pseudo labeled samples. In this way, the
detector can learn from both labeled and unlabeled data to become robust. In
this paper, we propose an interaction mechanism between a teacher and two
students to generate more reliable pseudo labels for unlabeled data, which are
beneficial to semi-supervised facial landmark detection. Specifically, the two
students are instantiated as dual detectors. The teacher learns to judge the
quality of the pseudo labels generated by the students and filter out
unqualified samples before the retraining stage. In this way, the student
detectors get feedback from their teacher and are retrained by premium data
generated by itself. Since the two students are trained by different samples, a
combination of their predictions will be more robust as the final prediction
compared to either prediction. Extensive experiments on 300-W and AFLW
benchmarks show that the interactions between teacher and students contribute
to better utilization of the unlabeled data and achieves state-of-the-art
performance.
