  Landmark localization in images and videos is a classic problem solved in
various ways. Nowadays, with deep networks prevailing throughout machine
learning, there are revamped interests in pushing facial landmark detection
technologies to handle more challenging data. Most efforts use network
objectives based on L1 or L2 norms, which have several disadvantages. First of
all, the locations of landmarks are determined from generated heatmaps (i.e.,
confidence maps) from which predicted landmark locations (i.e., the means) get
penalized without accounting for the spread: a high scatter corresponds to low
confidence and vice-versa. For this, we introduce a LaplaceKL objective that
penalizes for a low confidence. Another issue is a dependency on labeled data,
which are expensive to obtain and susceptible to error. To address both issues
we propose an adversarial training framework that leverages unlabeled data to
improve model performance. Our method claims state-of-the-art on all of the
300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in
the Wild (AFLW) dataset. Furthermore, our model is robust with a reduced size:
1/8 the number of channels (i.e., 0.0398MB) is comparable to state-of-that-art
in real-time on CPU. Thus, we show that our method is of high practical value
to real-life application.
