Cascade of forests for face alignment

In this study, we propose a regression forests-based cascaded method for face alignment. We build on the cascaded pose regression (CPR) framework and propose to use the regression forest as a primitive regressor. The regression forests are easier to train and naturally handle the over-fitting problem via averaging the outputs of the trees at each stage. We address the fact that the CPR approaches are sensitive to the shape initialisation; in contrast to using a number of blind initialisations and selecting the median values, we propose an intelligent shape initialisation scheme. More specifically, a large number of initialisations are propagated to a few early stages in the cascade, then only a proportion of them are propagated to the remaining cascades according to their convergence measurement. We evaluate the performance of the proposed approach on the challenging face alignment in the wild database and obtain superior or comparable performance with the state-of-the-art, in spite of the fact that we have utilised only the freely available public training images. More importantly, we show that the intelligent initialisation scheme makes the CPR framework more robust to unreliable initialisations that are typically produced by different face detections.