  This paper presents a comprehensive survey of facial feature point detection
with the assistance of abundant manually labeled images. Facial feature point
detection favors many applications such as face recognition, animation,
tracking, hallucination, expression analysis and 3D face modeling. Existing
methods can be categorized into the following four groups: constrained local
model (CLM)-based, active appearance model (AAM)-based, regression-based, and
other methods. CLM-based methods consist of a shape model and a number of local
experts, each of which is utilized to detect a facial feature point. AAM-based
methods fit a shape model to an image by minimizing texture synthesis errors.
Regression-based methods directly learn a mapping function from facial image
appearance to facial feature points. Besides the above three major categories
of methods, there are also minor categories of methods which we classify into
other methods: graphical model-based methods, joint face alignment methods,
independent facial feature point detectors, and deep learning-based methods.
Though significant progress has been made, facial feature point detection is
limited in its success by wild and real-world conditions: variations across
poses, expressions, illuminations, and occlusions. A comparative illustration
and analysis of representative methods provide us a holistic understanding and
deep insight into facial feature point detection, which also motivates us to
explore promising future directions.
