Learning spatial-temporal deformable networks for unconstrained face alignment and tracking in videos

Abstract

In this paper, we propose a spatial-temporal deformable networks approach to investigate both problems of face alignment in static images and face tracking in videos under unconstrained environments. Unlike conventional feature extractions which cannot explicitly exploit augmented spatial geometry for various facial shapes, in our approach, we propose a deformable hourglass networks (DHGN) method, which aims to learn a deformable mask to reduce the variances of facial deformation and extract attentional facial regions for robust feature representation. However, our DHGN is limited to extract only spatial appearance features from static facial images, which cannot explicitly exploit the temporal consistency information across consecutive frames in videos. For efficient temporal modeling, we further extend our DHGN to a temporal DHGN (T-DHGN) paradigm particularly for video-based face alignment. To this end, our T-DHGN principally incorporates with a temporal relational reasoning module, so that the temporal order relationship among frames is encoded in the relational feature. By doing this, our T-DHGN reasons about the temporal offsets to select a subset of discriminative frames over time steps, thus allowing temporal consistency information memorized to flow across frames for stable landmark tracking in videos. Compared with most state-of-the-art methods, our approach achieves superior performance on folds of widely-evaluated benchmarking datasets. Code will be made publicly available upon publication.