Robust discriminative regression for facial landmark localization under occlusion

Facial landmark localization or facial alignment is a crucial initial step in face analysis. The paper proposes a novel discriminative regression framework called Robust Discriminative Regression (RDR) for facial landmark localization. RDR framework consists of multiple partial feature regressors and a regression tree combination strategy. The proposed method copes with the partial facial landmarks invisible problem together with the optimization problem of multiple outputs combination. The RDR framework can be applied to both raw shape regression and model-based shape parameters regression. In model-based shape parameters regression we propose a two-level regression strategy, the first level is for rigid motion parameter regression and the second one is for non-rigid deformation parameter regression. Experiments on three widely used “face in-the-wild” databases (LFPW, COFW and IBUG) show that the proposed RDR outperforms other state-of-the-art facial landmark localization strategies in raw shape regression especially under partial occlusions or large pose variations. It also shows that the two-level regression strategy within RDR framework could achieve better performance than one-level parameters regression.