Multiscale recurrent regression networks for face alignment

In this paper, we propose an end-to-end multiscale recurrent regression networks (MSRRN) approach for face alignment. Unlike conventional face alignment methods by utilizing handcrafted features which require strong prior knowledge by hand, our MSRRN aims to seek a series of hierarchical feature transformations directly from image pixels, which exploits the nonlinear relationship between the face images to the positions of facial landmarks. To achieve this, we carefully design a recurrent regression network architecture, where the parameters across different stages are shared to memorize the shape residual descents between the initial shape and the ground-truth. To further improve the performance, our MSRNN learns to exploit the context-aware information from multiscale face inputs in a coarse-to-fine manner. Experimental results on the benchmarking face alignment datasets show the effectiveness of our approach.