Robust face alignment by cascaded regression and de-occlusion

Abstract

Face alignment is a typical facial behavior analysis task in computer vision. However, the performance of face alignment is degraded greatly when the face image is partially occluded. In order to achieve better mapping between facial appearance features and shape increments, we propose a robust and occlusion-free face alignment algorithm in which a face de-occlusion module and a deep regression module are integrated into a cascaded deep generative regression model. The face de-occlusion module is a disentangled representation learning Generative Adversarial Networks (GANs) which aims to locate occlusions and recover the genuine appearance from partially occluded face image. The deep regression module can enhance facial appearance representation by utilizing the recovered faces to obtain more accurate regressors. Then, by the cascaded deep generative regression model, we recover the partially occluded face image and achieve accurate locating of landmarks gradually. It is interesting to show that the cascaded deep generative regression model can effectively locate occlusions and recover more genuine faces, which can be further used to improve the performance of face alignment. Experimental results conducted on four challenging occluded face datasets demonstrate that our method outperforms state-of-the-art methods.