Robust facial landmark localization using multi partial features

The paper proposes a novel Robust Discriminative Regression (RDR) to handle the partial facial landmarks invisible problem for facial landmark localization. RDR consists of multiple partial feature regressors and a regression tree combination strategy to copes with the partial invisible problem together with the optimal multi output combination problem. The RDR is implemented with SIFT features and Linear Regression to achieve the balance of accuracy and computation efficiency. Experiments on two widely used “face in-the-wild” databases (LFPW and COFW) show that the proposed RDR outperforms other state-of-the-art facial landmark localization methods especially in the cases of partial occlusion and large pose variation.