  This paper introduces a novel real-time algorithm for facial landmark
tracking. Compared to detection, tracking has both additional challenges and
opportunities. Arguably the most important aspect in this domain is updating a
tracker's models as tracking progresses, also known as incremental (face)
tracking. While this should result in more accurate localisation, how to do
this online and in real time without causing a tracker to drift is still an
important open research question. We address this question in the cascaded
regression framework, the state-of-the-art approach for facial landmark
localisation. Because incremental learning for cascaded regression is costly,
we propose a much more efficient yet equally accurate alternative using
continuous regression. More specifically, we first propose cascaded continuous
regression (CCR) and show its accuracy is equivalent to the Supervised Descent
Method. We then derive the incremental learning updates for CCR (iCCR) and show
that it is an order of magnitude faster than standard incremental learning for
cascaded regression, bringing the time required for the update from seconds
down to a fraction of a second, thus enabling real-time tracking. Finally, we
evaluate iCCR and show the importance of incremental learning in achieving
state-of-the-art performance. Code for our iCCR is available from
http://www.cs.nott.ac.uk/~psxes1
