Extended Supervised Descent Method for Robust Face Alignment

Abstract. Supervised Descent Method (SDM) is a highly efficient and accurate approach for facial landmark locating/face alignment. It learns a sequence of descent directions that minimize the difference between the estimated shape and the ground truth in HOG feature space during training, and utilize them in testing to predict shape increment itera-tively. In this paper, we propose to modify SDM in three respects: 1) Multi-scale HOG features are applied orderly as a coarse-to-fine feature detector; 2) Global to local constraints of the facial features are con-sidered orderly in regression cascade; 3) Rigid Regularization is applied to obtain more stable prediction results. Extensive experimental result-s demonstrate that each of the three modifications could improve the accuracy and robustness of the traditional SDM methods. Furthermore, enhanced by the three-fold improvements, the extended SDM compares favorably with other state-of-the-art methods on several challenging face data sets, including LFPW, HELEN and 300 Faces in-the-wild.

Face Alignment Assisted by Head Pose Estimation

In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation. We first investigate the failure cases of most state of the art face alignment approaches and observe that these failures often share one common global property, i.e. the head pose variation is usually large. Inspired by this, we propose a deep convolutional network model for reliable and accurate head pose estimation. Instead of using a mean face shape, or randomly selected shapes for cascaded face alignment initialisation, we propose two schemes for generating initialisation: the first one relies on projecting a mean 3D face shape (represented by 3D facial landmarks) onto 2D image under the estimated head pose; the second one searches nearest neighbour shapes from the training set according to head pose distance. By doing so, the initialisation gets closer to the actual shape, which enhances the possibility of convergence and in turn improves the face alignment performance. We demonstrate the proposed method on the benchmark 300W dataset and show very competitive performance in both head pose estimation and face alignment.Comment: Accepted by BMVC201