We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment. Our proposed model predicts 2D facial point maps regularized by a regression loss, while uniquely exploiting recurrent learning at both spatial and temporal dimensions. At the spatial level, we add a feedback loop connection between the combined output response map and the input, in order to enable iterative coarse-to-fine face alignment using a single network model. At the temporal level, we first decouple the features in the bottleneck of the network into temporalvariant factors, such as pose and expression, and temporalinvariant factors, such as identity information. Temporal recurrent learning is then applied to the decoupled temporalvariant features, yielding better generalization and significantly more accurate results at test time. We perform a comprehensive experimental analysis, showing the importance of each component of our proposed model, as well as superior results over the state-of-the-art in standard datasets.
Xi et al. REF propose a recurrent encoder-decoder network (RED), combined with spatial and temporal recurrent learning, to explicitly model the temporal dependency relationship on frames.
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
ArXiv
ArXiv
Computer Science