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Traditional face alignment based on machine learning usually tracks thelocalizations of facial landmarks employing a static model trained offlinewhere all of the training data is available in advance. When new trainingsamples arrive, the static model must be retrained from scratch, which isexcessively time-consuming and memory-consuming. In many real-timeapplications, the training data is obtained one by one or batch by batch. Itresults in that the static model limits its performance on sequential imageswith extensive variations. Therefore, the most critical and challenging aspectin this field is dynamically updating the tracker's models to enhancepredictive and generalization capabilities continuously. In order to addressthis question, we develop a fast and accurate online learning algorithm forface alignment. Particularly, we incorporate on-line sequential extremelearning machine into a parallel cascaded regression framework, coinedincremental cascade regression(ICR). To the best of our knowledge, this is thefirst incremental cascaded framework with the non-linear regressor. One mainadvantage of ICR is that the tracker model can be fast updated in anincremental way without the entire retraining process when a new input isincoming. Experimental results demonstrate that the proposed ICR is moreaccurate and efficient on still or sequential images compared with the recentstate-of-the-art cascade approaches. Furthermore, the incremental learningproposed in this paper can update the trained model in real time.

A fast online cascaded regression algorithm for face alignment