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We propose a novel method for real-time face alignment in videos based on arecurrent encoder-decoder network model. Our proposed model predicts 2D facialpoint heat maps regularized by both detection and regression loss, whileuniquely exploiting recurrent learning at both spatial and temporal dimensions.At the spatial level, we add a feedback loop connection between the combinedoutput response map and the input, in order to enable iterative coarse-to-fineface alignment using a single network model, instead of relying on traditionalcascaded model ensembles. At the temporal level, we first decouple the featuresin the bottleneck of the network into temporal-variant factors, such as poseand expression, and temporal-invariant factors, such as identity information.Temporal recurrent learning is then applied to the decoupled temporal-variantfeatures. We show that such feature disentangling yields better generalizationand significantly more accurate results at test time. We perform acomprehensive experimental analysis, showing the importance of each componentof our proposed model, as well as superior results over the state of the artand several variations of our method in standard datasets.

RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment