Learning Cascaded Deep Auto-Encoder Networks for Face Alignment
In this paper, we propose a new cascaded deep auto-encoder networks (CDAN) approach for face alignment. Our framework consists of a global exemplar-based deep auto-encoder network (GEDAN) and a series of localized deep auto-encoder networks (LDAN) in a cascaded fashion. The global network takes a low-resolution holistic facial image as input and generates a preliminary facial landmark configuration. The following localized networks sample pose-indexed local features around current landmark positions, and refine the landmark positions with increasingly higher image resolutions. Our network architectures are designed to achieve greater robustness against pose variations as well as higher landmark estimation accuracy. Experimental results on three datasets show that the proposed approach achieves superior alignment accuracy with real-time speed.