  Traditional face alignment based on machine learning usually tracks the
localizations of facial landmarks employing a static model trained offline
where all of the training data is available in advance. When new training
samples arrive, the static model must be retrained from scratch, which is
excessively time-consuming and memory-consuming. In many real-time
applications, the training data is obtained one by one or batch by batch. It
results in that the static model limits its performance on sequential images
with extensive variations. Therefore, the most critical and challenging aspect
in this field is dynamically updating the tracker's models to enhance
predictive and generalization capabilities continuously. In order to address
this question, we develop a fast and accurate online learning algorithm for
face alignment. Particularly, we incorporate on-line sequential extreme
learning machine into a parallel cascaded regression framework, coined
incremental cascade regression(ICR). To the best of our knowledge, this is the
first incremental cascaded framework with the non-linear regressor. One main
advantage of ICR is that the tracker model can be fast updated in an
incremental way without the entire retraining process when a new input is
incoming. Experimental results demonstrate that the proposed ICR is more
accurate and efficient on still or sequential images compared with the recent
state-of-the-art cascade approaches. Furthermore, the incremental learning
proposed in this paper can update the trained model in real time.
