  In this paper, we explore methods of robustly extracting fiducial facial
feature points - an important process for numerous facial image processing
tasks. We consider various methods to first detect face, then facial features
and finally salient facial feature points. Colour-based models are analysed and
their overall unsuitability for this task is summarised. The bulk of the report
is then dedicated to proposing a learning-based method centred on the
Viola-Jones algorithm. The specific difficulties and considerations relating to
feature point detection are laid out in this context and a novel approach is
established to address these issues. On a sequence of clear and unobstructed
face images, our proposed system achieves average detection rates of over 90%.
Then, using a more varied sample dataset, we identify some possible areas for
future development of our system.
