Coarse-to-Fine Supervised Descent Method for Face Alignment

Abstract

Supervised Descent Method (SDM) is an efficient and accurate approach for facial landmark locating and face alignment. In the training phase, it requires a large amount of training samples to learn the descent directions and get the corresponding regressors. Then in the test phase, it uses the corresponding regressors to estimate the descent directions and locate the facial landmarks. However, when the facial expression or direction changes too much, generally SDM cannot obtain good performance due to the large variation between the initial shape (the initial shape of SDM is the mean shape of the training samples) and the target shape. Therefore, we propose a coarse-to-fine SDM (CFSDM) method to improve the accuracy of the test results. This method predicts the approximate coordinates of the facial landmarks with a simple CNN (Convolutional Neural Network) network (here we introduce the channel-wise attention mechanism, which can predict the coordinates of the landmarks more accurately with a relatively simple structure) in advance, and then SDM will take the coordinates as its initial shape’s coordinates, which reduces the distance between the initial shape and the target shape, thereby solving the problem that SDM cannot achieve good results when the facial expression or direction changes greatly.