  A face model is a mathematical representation of the distinct features of a
human face. Traditionally, face models were built using a set of fiducial
points or landmarks, each point ideally located on a facial feature, i.e.,
corner of the eye, tip of the nose, etc. Face alignment is the process of
fitting the landmarks in a face model to the respective ground truth positions
in an input image containing a face. Despite significant research on face
alignment in the past decades, no review analyses various face models used in
the literature. Catering to three types of readers - beginners, practitioners
and researchers in face alignment, we provide a comprehensive analysis of
different face models used for face alignment. We include the interpretation
and training of the face models along with the examples of fitting the face
model to a new face image. We found that 3D-based face models are preferred in
cases of extreme face pose, whereas deep learning-based methods often use
heatmaps. Moreover, we discuss the possible future directions of face models in
the field of face alignment.
