  Localization of salient facial landmark points, such as eye corners or the
tip of the nose, is still considered a challenging computer vision problem
despite recent efforts. This is especially evident in unconstrained
environments, i.e., in the presence of background clutter and large head pose
variations. Most methods that achieve state-of-the-art accuracy are slow, and,
thus, have limited applications. We describe a method that can accurately
estimate the positions of relevant facial landmarks in real-time even on
hardware with limited processing power, such as mobile devices. This is
achieved with a sequence of estimators based on ensembles of regression trees.
The trees use simple pixel intensity comparisons in their internal nodes and
this makes them able to process image regions very fast. We test the developed
system on several publicly available datasets and analyse its processing speed
on various devices. Experimental results show that our method has practical
value.
