Pose-Robust Face Alignment with Adaptive Supervised Descent Method

Face alignment is a key component in facial processing. It is a challenging task because human facial images in real-world usually contain large variations due to the differences in pose, illumination, etc. Shape init-ialization and feature extraction are crucial in face landmark alignment. This paper proposes a pose-robust face alignment model based on adaptive supervised descent method (SDM). Firstly, in order to reduce the influence of pose differences for face alignment, this paper uses clustering algorithm to cluster the face images into three categories (frontal faces, left faces, right faces) according to pose. Thus, the pose in each cluster is more compact. Secondly, face alignment can be taken as a coarse-to-fine supervised learning process with multi-stage. Therefore, the adaptive block size of feature extraction (from big to small) is used to get discriminative features. Based on the above two strategies, within each cluster, a better initial shape is given and the discriminant regression model is trained for facial landmark localization via adaptive SDM. A series of experiments have been conducted on benchmark datasets LFPW, HELEN and 300W. The experimental results show that this method makes facial landmark localization accurately in large pose images, and demonstrate the superiority of the proposed method by comparing with existing methods