Robust facial landmark localization using classified random ferns and pose-based initialization

Facial landmark localization on images, which are captured in the wild under uncontrolled conditions, is a challenge work. Compared with localization methods using deformable models, the cascaded regression models obtain much better performance in terms of accuracy and efficiency for those unseen images. However, such regression approach is initialization dependent. In this paper, we propose a new random fern constructing method by introducing classification analysis. A robust pose-based initialization method is proposed by computing the pose similarity between the estimated image and initial shapes. The proposed method is evaluated on two “in the wild” databases, LFPW and HELEN, which are collected from websites and largely various in pose, illumination, expression and occlusion. The experimental results have demonstrated that the proposed method can significantly enhance the accuracy and robustness on facial landmark localization.