We describe a detector of facial landmarks based on the Deformable Part Models.
We treat the task of landmark detection as an instance of the structured output
classification problem. We propose to learn the parameters of the detector from
data by the Structured Output Support Vector Machines algorithm. In contrast to
the previous works, the objective function of the learning algorithm is
directly
related to the performance of the resulting detector which is controlled by a
user-defined loss function. The resulting detector is real-time on a standard
PC, simple to implement and it can be easily modified for detection of a
different set of landmarks. We evaluate performance of the proposed landmark
detector on a challenging ``Labeled Faces in the Wild'' (LFW) database. The
empirical results demonstrate that the proposed detector is consistently more
accurate than two public domain implementations based on the Active Appearance
Models and the Deformable Part Models. We provide an open-source implementation
of the proposed detector and the manual annotation of the facial landmarks for
all images in the LFW database.