  Dense facial landmark detection is one of the key elements of face processing
pipeline. It is used in virtual face reenactment, emotion recognition, driver
status tracking, etc. Early approaches were suitable for facial landmark
detection in controlled environments only, which is clearly insufficient.
Neural networks have shown an astonishing qualitative improvement for
in-the-wild face landmark detection problem, and are now being studied by many
researchers in the field. Numerous bright ideas are proposed, often
complimentary to each other. However, exploration of the whole volume of novel
approaches is quite challenging. Therefore, we present this survey, where we
summarize state-of-the-art algorithms into categories, provide a comparison of
recently introduced in-the-wild datasets (e.g., 300W, AFLW, COFW, WFLW) that
contain images with large pose, face occlusion, taken in unconstrained
conditions. In addition to quality, applications require fast inference, and
preferably on mobile devices. Hence, we include information about algorithm
inference speed both on desktop and mobile hardware, which is rarely studied.
Importantly, we highlight problems of algorithms, their applications,
vulnerabilities, and briefly touch on established methods. We hope that the
reader will find many novel ideas, will see how the algorithms are used in
applications, which will enable further research.
